%%% On Symmetry of Uniform and Preferential Attachment Graphs
%%% Abram Magner,  Svante Janson,  Giorgos Kollias, Wojciech Szpankowski
%%% 10 January 2014. Revised 12 August 2014

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% Author information here.
\author{
Abram Magner\thanks{Department of Computer Science, Purdue University, Indiana, U.S.A.  anmagner@purdue.edu}\\
%\small Department of Computer Science\\[-0.8ex]
%\small Purdue University\\[-0.8ex] 
%\small Indiana, U.S.A.\\
%\small\tt anmagner@purdue.edu\\
\and
Svante Janson\thanks{Department of Mathematics, Uppsala University, Uppsala, Sweden.  S. Janson partly supported by the Knut and Alice Wallenberg Foundation.  svante@math.uu.se}\\
%\small Department of Mathematics\\[-0.8ex]
%\small Uppsala University\\[-0.8ex]
%\small Uppsala, Sweden\\
%\small\tt svante@math.uu.se\\
\and
Giorgos Kollias\thanks{Thomas J. Watson Research Center, Yorktown Heights, New York, U.S.A.  gkollias@us.ibm.com}\\ %\footnotemark[1] \\
%\small Thomas J. Watson Research Center\\[-0.8ex]
%\small Yorktown Heights\\[-0.8ex]
%\small New York, U.S.A.\\
%\small\tt gkollias@us.ibm.com\\
\and
Wojciech Szpankowski\thanks{Department of Computer Science, Purdue University, Indiana, U.S.A.; also
        with the Faculty of Electronics, Telecommunications and Informatics, Gda\'{n}sk University of Technology, Poland.  spa@cs.purdue.edu}
        %\footnotemark[1]   \\
%\small Department of Computer Science\\[-0.8ex]
%\small Purdue University\\[-0.8ex]
%\small Indiana, U.S.A.\\
%\small\tt spa@cs.purdue.edu
}


% \date{\dateline{submission date}{acceptance date}\\
% \small Mathematics Subject Classifications: comma separated list of
% MSC codes available from http://www.ams.org/mathscinet/freeTools.html}

\date{\small Submitted: \today\\%\dateline{May 29, 2014}{}{}\\
\small Mathematics Subject Classifications: 05C80, 05C82}       % Random graphs, small world graphs/complex networks.


%\author[A. Magner, S. Janson, G. Kollias, and W. Szpankowski]
%{Abram Magner\addressmark{1}\footnotemark[1]\footnotetext{
%This work was supported by
%        NSF Center for Science of Information (CSoI) Grant CCF-0939370,
%        NSA Grants H98230-11-1-0184 and H98230-11-1-0141, and in addition
%        NSF Grants DMS-0800568, and CCF-0830140, 
%        and the MNSW grant DEC-2013/09/B/ST6/02258. W. Szpankowski is also
%        a Visiting Professor at ETI, Gda\'{n}sk University of
%        Technology, Poland.
%S. Janson partly supported by the Knut and Alice Wallenberg Foundation.
%}
%\and
%Svante Janson\addressmark{2}
%\and
%Giorgos Kollias\addressmark{3}\footnotemark[1]
%\and
%Wojciech Szpankowski\addressmark{1}\footnotemark[1]
%}
%
\title
{\bf On Symmetry of Uniform and Preferential Attachment Graphs\thanks{This work was supported by
        NSF Center for Science of Information (CSoI) Grant CCF-0939370,
        NSA Grant H98230-11-1-0141, and in addition
        NSF Grants DMS-0800568, and CCF-0830140, 
        and the MNSW grant DEC-2013/09/B/ST6/02258.}
}

%\address{
%    \addressmark{1}
%    Department of Computer Science, 
%    Purdue University,  
%    Indiana, U.S.A. \\
%    \addressmark{2}
%    Department of Mathematics,
%    Uppsala University, 
%    Uppsala, Sweden \\
%    \addressmark{3}
%    Thomas J. Watson Research Center, 
%    Yorktown Heights, 
%    NY USA
%}
%\date{28 May, 2014}
%\keywords{preferential attachment, symmetry, automorphism, random graphs}

\begin{document}

\maketitle

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{abstract}
%\paragraph{Abstract.}
    Motivated by the problem of graph structure compression under realistic 
    source models, we study the symmetry behavior of preferential and uniform 
    attachment graphs. These are two dynamic models of network growth 
    in which new nodes 
    attach to a constant number $m$ of existing ones according to some 
    attachment scheme.  We prove symmetry results for $m=1$ and $2$, and we 
    conjecture that for $m\geq 3$, both models yield asymmetry with high 
    probability.  We provide new empirical evidence in terms of graph defect.  
    We also prove that vertex defects in the uniform attachment model grow at 
    most logarithmically with graph size, then use this to prove a weak 
    asymmetry result for all values of $m$ in the uniform attachment model.  
    Finally, we introduce a natural variation of the two models that 
    incorporates preference of new nodes for nodes of a similar age, and we 
    show that the change introduces symmetry for all values of $m$.
\end{abstract}

\section{Introduction}
% Motivation: Compression of graphical structures.
%Study of the asymptotic behavior of the automorphism group of a random graph
%started with \cite{erdosrenyi1963}, wherein Erd\H{o}s-R\'{e}nyi graphs with
%constant connection probability were shown to be asymmetric with high probability,
%a result motivated by the combinatorial question of determining the asymptotics
%of the number of unlabeled graphs on $n$ vertices for $n \to \infty$.  A
%similar question motivated the investigation of symmetry properties of random
%regular graphs \cite{bollobas1982, kimvusudakov2002}.

Study of the asymptotic behavior of the symmetries of random graphs,
originally motivated by combinatorial problems, has relatively recently
found a new application in the problem of compression of graph structures. 
The basic problem can be formulated as follows: given a probability distribution 
on labeled graphs, determine an encoding of graph structures (that is, 
\emph{unlabeled} graphs) so as to minimize expected description length.

%Choi and Szpankowski 
\cite{choiszpa2009} studied this problem in the setting of
Erd\H{o}s-R\'{e}nyi graphs. They showed that, under any distribution
giving equal probability to isomorphic graphs, the entropy of the induced
distribution on graph structures (i.e., isomorphism classes of graphs) is less
than the entropy of the original distribution by an amount proportional to the
expected logarithm of the number of automorphisms. Thus the solution to the
above problem is intimately connected with the symmetries of the random graph
model under consideration.

Study of symmetries is further motivated by their connection to various
measures of information contained in a graph structure.  For instance, 
the \emph{topological entropy} of a random graph, studied by \cite{rashevsky1955} 
and \cite{trucco1956}, measures the uncertainty in the orbit class (i.e., the 
set of nodes having the same long-term neighborhood structure) of a node 
chosen uniformly at random from the node set of the graph.  If the graph 
is asymmetric with high probability, then the topological entropy is 
maximized: if $n$ is the size of the graph, then the topological entropy 
is, to leading order, $\log n$.  In general, if the symmetries of the 
graph can be characterized precisely, then so can the topological entropy.
Furthermore, tools developed here will allow us to study and compare 
topological information of nodes (i.e., by how many bits a graph view
of one node differs from another).


The present paper is a first step toward the goal of extending graph structure
compression results to other random graph models.  In particular, many
real-world graphs, such as biological and social networks, exhibit a power
law degree distribution (see \cite{durrett2006}). 
To explain this phenomenon, \cite{barabasialbert2002}
proposed the \emph{preferential attachment} mechanism, in which a graph is built
one vertex at a time, and each new vertex $t$ attaches to a given old vertex
$v$ with 
probability proportional to the current degree of $v$.
Thus, we study a variant of a preferential attachment model. The primary problem
appears to be difficult, so we also study a closely related model in which 
attachment is uniform, in the hope that the proof techniques used there may be
generalized.  In both uniform and preferential attachment models, we prove 
that when each new vertex chooses only one previous vertex as a neighbor,
there is symmetry with high probability, and when each new vertex makes two
choices, there is a positive probability of symmetry.  In addition, we determine
the asymptotic behavior of a quantity known as the \emph{defect} of a vertex,
introduced by \cite{kimvusudakov2002}, which measures the extent to which the neighborhood of 
the vertex contributes to asymmetry of the graph.  We then use this to prove
a weak asymmetry result for the uniform attachment case.

We also introduce the \emph{sliding window} model, a dynamic model
in which new vertices choose neighbors from within windows of expected size
uniformly bounded above by a constant, the purpose being to exhibit a ``natural''
mechanism that, coupled with a quite general attachment scheme that includes
preferential and uniform attachment as special cases, results in symmetry with
asymptotically positive probability.

% Explain what's known.

Study of the asymptotic behavior of the automorphism group of a random graph
started with \cite{erdosrenyi1963}, wherein Erd\H{o}s-R\'{e}nyi graphs with
constant connection probability were shown to be asymmetric with high probability,
a result motivated by the combinatorial question of determining the asymptotics
of the number of unlabeled graphs on $n$ vertices for $n \to \infty$.  A
similar question motivated the investigation of symmetry properties of random
regular graphs by \cite{bollobas1982} and \cite{kimvusudakov2002}.  In the latter
paper, the authors precisely characterized the range for which 
Erd\H{o}s-R\'{e}nyi graphs are asymmetric by proving concentration results
for random variables defined in terms of vertex defect.  They then proved
an asymmetry result for random regular graphs using the previous result.

% Explain why it's hard.
For general models, symmetry and asymmetry results can be nontrivial to prove,
due to the non-monotonicity of the properties considered.  Furthermore, the
particular models considered here present more difficulties not seen in
the Erd\H{o}s-R\'{e}nyi case: there is significant dependence between edge
events, and graph sparseness makes derivation of concentration results
difficult. 

% Motivation: Compression of graphical structures.  Specifically, power law
% graphs.
% Motivation: Combinatorics; counting of unique structures.

% What makes it difficult: Non-monotonicity of symmetry, graph sparseness,
% and dependence.


% Give outline of the rest of the paper.
The rest of the paper is organized as follows: in Section \ref{MainResults}, we
formally state the models and the main problem; we then state the main results,
along with a discussion of their significance.  We also present some
empirical validation of the symmetry results, as well as evidence in support 
of the asymmetry conjecture.  Finally, in Section \ref{Proofs}, we 
give the proofs. 


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Main Results}
\label{MainResults}
In this section, we state the main problems, introduce the models that we
consider, and formulate the main results.
First, we introduce some standard graph-theoretic terminology and notation.  
We start with the notion of structure-preserving 
transformations between labeled graphs: given two graphs $G_1$ and $G_2$ with
vertex sets $V(G_1)$ and $V(G_2)$, a mapping $\phi:V(G_1)\to V(G_2)$ is said
to be an \emph{isomorphism} if it is bijective and preserves edge relations;
that is, for any $x, y\in V(G_1)$, there is an edge between $x$ and $y$ if
and only if there is an edge in $G_2$ between $\phi(x)$ and $\phi(y)$.  When
such a $\phi$ exists, $G_1$ and $G_2$ are said to be \emph{isomorphic}; that
is, they have the same structure.

An isomorphism from the vertices of a graph $G$ to itself is called an
\emph{automorphism} or \emph{symmetry}.  The set of automorphisms of $G$,
together with the operation of function composition, forms a group, which
is called the \emph{automorphism group} of $G$, denoted by $\Aut(G)$.  Note
that the image of $G$ under any of its symmetries is $G$, the same labeled 
graph.

We then say that $G$ is \emph{symmetric} if it has at least one {\it nontrivial
symmetry} and that $G$ is \emph{asymmetric} if the only symmetry of $G$ is
the identity permutation.  Intuitively, $G$ is symmetric if and only if
there are at least two vertices whose graph perspectives are the same; that is, 
their neighborhoods at any distance have the same structure.

%Finally, we say that a permutation $\pi$ \emph{fixes} a vertex $u$ if $\pi(u) =
%u$.

The main problem can then be stated as follows: given a random graph 
process $\{G_n\}_{n\geq 1}$, characterize the behavior of its 
automorphism group for $n\to\infty$.

\subsection{Definitions of Models}
In what follows, vertices of an $n$-vertex graph are the elements of the set
$[n] := \{1, \dots, n\}$.
%\begin{definition}[Uniform attachment model]
%    A uniform attachment graph $\UA(n,m)$ on $n$ vertices, with parameter
%    $m$, is constructed as follows: at time $t=1$, a single vertex with name
%    $1$ is added.  For each time $1 < t \leq n$, a vertex with name $t$ is 
%    added, and $m$ vertices $c_{t1}, \cdots, c_{tm}$ in $[t-1]$ are chosen 
%    independently, uniformly at random, and with replacement.  An edge 
%    between $t$ and $v$ is added if and only if $c_{ti} = v$ for some $i$. 
%\end{definition}

% Preferential attachment!
A preferential attachment model is a dynamic model of network growth in which
new vertices, when they choose vertices already in the graph as neighbors,
have a preference for a given vertex that is proportional to its current
degree, 
see \cite{barabasialbert2002}. 
Thus, nodes with high degree tend to be preferred for new connections.
The following definition formalizes this.
(A slightly different formalization of the Barab\'{a}si--Albert  model is 
given by \cite{BollobasRiordan2004}.)

\begin{definition}[Preferential attachment model]
    A preferential attachment graph $\PA(n,m)$ on $n$ vertices, with parameter
    $m$, is constructed as follows: at time $t=1$, a single vertex with name
    $1$ and attractiveness $\att_1(1)=0$ is added.  For each time $1 < t \leq n$, a 
    vertex with name $t$ is added, and $m$ vertices $c_{t1}, \dots, c_{tm}$ in
    $[t-1]$ are chosen with replacement such that
    \[
        \Pr[c_{tj} = v] 
        = \frac{\att_v(t-1)}{\sum_{w=1}^{t-1} \att_w(t-1)}
        = \frac{\att_v(t-1)}{2m(t-2)}.    
    \]
    (Here we adopt the convention that $0/0 = 1$.)
    An edge between $t$ and $v$ is added if and only if $c_{ti} = v$ for some $i$.
    For each $v \in [t-1]$, we set $\att_v(t) = \att_v(t-1) + |\{ j | c_{tj}=v \}|$.
    Finally, we set $\att_t(t) = m$.
\end{definition}

Another way to express this 
is to first construct a growing multigraph, where
we at each step add one new vertex and $m$ edges from it, with the other
endpoints chosen at random with replacement as above; then $\att_v(t)$ equals
the degree of $v$ at time $t$. 
We then reduce any set of multiple edges to a single edge to
obtain the simple graph  $\PA(n,m)$.

We will also consider a variation, which we call the \emph{uniform attachment} 
model, with the only change being that vertex choices are now equiprobable;
that is, we now fix $\att_v(t-1) = 1$ for all $t$ and $v \in [t-1]$.  
(For $m=1$ this yields the well-known \emph{random recursive tree} (see \cite{smythe1994}).) 
The rationale for studying this simpler model is that solving our symmetry 
problems poses many of the same challenges for both models: both, for example, 
generate sparse graphs, which seems to rule out an approach to proving asymmetry based on defect (discussed below);
furthermore, in both models, in considering a neighborhood of a vertex, one must
distinguish between incoming and outgoing vertices, which complicates other possible approaches to asymmetry proofs. 
On the other hand, the uniform attachment model is advantageous, in that  we need not deal with 
the dependence resulting from the preferential attachment mechanism.

We also study another practical variant of the attachment model called
the sliding window model that we define next.

% Sliding window!
\begin{definition}[Sliding window model]
    The sliding window model with random window size works as follows: at time
    $1$, vertex $1$ is added.  At time $t > 1$, vertex $t$ is added, and a window
    size $W_t$, taking values in $\{2, \dots,t-1\}$, is chosen according to the distribution
    function $F_t$, independent of anything else.  Then, $m$ vertices are
    chosen with replacement from the set $[t-W_t, t-1]$ (which we call the 
    \emph{window} of vertex $t$), with the distribution of each choice $c_{tj}$ 
    determined by the ratio of the attractiveness of any node in the window
    to the total attractiveness of the window.  That is,
    \[
        \Pr[c_{tj} = v | W_t = w]
        = \frac{\att_{v}(t-1)}{\sum_{k=t-w}^{t-1} \att_k(t-1)}.
    \]
    Here, $\att_v(x)$ denotes the attractiveness of vertex $v$ at time $x$.
    In the preferential attachment sliding window model, $\att_v(x)$ is given
    by
    \[
        \att_v(x) = m + \sum_{k=1}^{x-1} \sum_{j=1}^m [c_{kj} = v].
    \]
    In the uniform attachment version, $\att_v(x) = 1$.
\end{definition}

\subsection{Statement of Results}
The first result characterizes the expected vertex defect for the uniform 
attachment model.  Vertex, permutation, and graph defect were introduced by
\cite{kimvusudakov2002} in order to prove asymmetry for Erd\H{o}s-R\'{e}nyi
graphs.  The definitions are as follows.

\begin{definition}[Defect]
% Vertex defect.
Fix a graph $G$ on $n$ vertices.  Given a permutation $\pi \in S_n$ and 
$u \in [n]$, we define the defect of $u$ with respect to $\pi$ to be
\[
    D_\pi(u) = |\symmdiff|,
\]    
where $N(x)$ denotes the set of neighbors of vertex $x$, and $\triangle$
denotes the symmetric difference of two sets.
% Permutation defect.
We define the defect of $\pi$ to be
\[
    D_\pi(G) = \max_{u\in[n]} D_\pi(u).
\]    
% Graph defect.
Finally, we define the defect of $G$ to be
\[
    D(G) = \min_{\pi \neq \id} D(\pi).
\]    
\end{definition}

Some simple consequences of these definitions are as follows: for a graph $G$ on $n$
vertices and a permutation $\pi \in [n]$, $\pi$ is an automorphism of $G$ if and only if 
$D_\pi(G) = 0$, which is equivalent to non-existence of a vertex $u \in [n]$ such that 
$D_\pi(u) \neq 0$; $G$ has nontrivial symmetries if and only if $D(G) = 0$.  Thus,
vertex defect measures the extent to which a given vertex's neighborhood structure
breaks the symmetry of $\pi$, permutation defect measures the number of edges adjacent
to any particular vertex that need to be modified in order to make $\pi$ a symmetry of $G$, 
and graph defect measures the number of edges 
adjacent to any vertex 
that need to be modified in
order to  
introduce a nontrivial symmetry into $G$.

For a nontrivial permutation $\pi \in S_n$ and any $u \in [n]$, define 
$\omega(\pi, u) = \min\{u, \pi(u)\}$.  We also define $\omega(\pi)$ to be the
minimum vertex not fixed by $\pi$ (where we say that $\pi$ \emph{fixes} a
vertex $w$ if $\pi(w) = w$).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{theorem}[Expected defect for a vertex]
    \label{ExpectedDefectTheorem}
    Fix $m \in \N$ in the uniform attachment model.
    For any $n$ sufficiently large, $\pi \neq \id, \pi \in S_n$, and 
    $u \in [n]$ not fixed by $\pi$,
    \[
    \log\left( \frac{n}{\max\{\omega(\pi, u) + 2, (2m+2)\}} \right)
    \leq \E[D_{\pi}(u)]
    \leq 1 + 4m \left( 2 + \log\left( \frac{n}{\omega(\pi, u)} \right) \right).
    \]
\end{theorem}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
This theorem is significant for two reasons: it plays a central role in the
proof of Theorem \ref{DefectProbabilityTheorem}, and it gives an indication
that an approach to an asymmetry proof via defects, as used in the setting of 
Erd\H{o}s-R\'{e}nyi graphs (see \cite{kimvusudakov2002}), may not be fruitful.
A key difference between the Erd\H{o}s-R\'{e}nyi model and the uniform and
preferential attachment ones is that the expected defect in the former is
$\Theta(np(1-p))$ for $p, 1-p \gg \frac{\log n}{n}$, which is essential for 
the proof technique used for that model to work.


%% Explain Kim, Vu, Sudakov proof.
%More specifically, a key step in the asymmetry proof for Erd\H{o}s-R\'{e}nyi 
%graphs was establishing sufficiently tight concentration away from $0$ for a 
%random variable $X$ defined to be a sum of vertex defects with respect to a 
%permutation $\pi$ that fixes all but $k$ vertices, where the sum is over all
%$k$ non-fixed vertices.  Standard concentration results, such as the 
%Azuma-Hoeffding inequality \cite{szpa2001Book}, cannot be used here because
%$\E[X]$ is such that the exponent in any of these inequalities does not 
%tend to $-\infty$ for all values of $k$.  Thus, our Theorem~\ref{ExpectedDefectTheorem}
%suggests that a different approach is needed.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Discuss theorem 2!
The previous theorem can be used to derive a {\it weak asymmetry} result for 
the uniform attachment model as follows: for a given sequence of 
permutations $\pi_n \neq \id$, to show that $\pi_n \notin \Aut(G_n)$ with high
probability, it is sufficient to exhibit a sequence of vertices $u_n$
such that $\lim_{n\to\infty} \Pr[D_{\pi_n}(u_n)=0] = 1$.
In particular, we can choose $u_n = \omega(\pi_n)$, the minimum non-fixed
vertex of $\pi_n$.  
We prove the following result.
%Now, the set of permutation sequences can be 
%partitioned into three classes: those for which $u_n = O(1)$, those
%for which $u_n \xrightarrow{n\to\infty} \infty$, and those for which
%neither is true.  In the $u_n = O(1)$ case, the expected defect of $u$
%tends to infinity with $n$ at a logarithmic rate; a second moment method
%approach yields the result.  In the $u_n \to \infty$ case, direct
%computations and elementary bounds suffice.  The remaining case can be
%easily reduced to the other two by splitting the permutation sequence
%into two subsequences, which can be handled by the arguments used in the
%previous two cases.  This results in the following.
\begin{theorem}[Probability of vertex defect being $0$]
    \label{DefectProbabilityTheorem}
    Fix $m \geq 1$ and consider a sequence of graphs 
    in the uniform attachment model $G_n \distrib \UA(n, m)$.
    Let $\{\pi_n\}_{n=1}^\infty, \pi_n \in S_n-\{\id\}$, and, for each $n$, let 
    $u_n = \omega(\pi_n)$.  Then
    \[
        \Pr[D_{\pi_n}(u_n) = 0] \xrightarrow{n\to\infty} 0,
    \]
    so that the asymptotic probability that $\pi_n \in \Aut(G_n)$ is $0$.
\end{theorem}

We remark that we call this a weak asymmetry result because it is a statement
about which permutations are \emph{not} in the automorphism group of $G_n$:
any given sequence of permutations (or small sets of permutations) is asymptotically 
not likely to be in the automorphism group of a growing uniform attachment graph.
Thus, this result has the flavor of an asymmetry result.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure}%[htbp]
    %\centering
    %\begin{subfigure}%{\the\textwidth}
    %    \centering
    %    \includegraphics[width=5cm]{M1Figure}
    %\end{subfigure}%
    %\begin{subfigure}%{\textwidth 0pt}
    %    \centering
    %    \includegraphics[scale=.6]{M2Figure}
    %\end{subfigure}%
    %\hfill


% Discuss Theorem 3!
    \begin{center}
        \subfigure[\textwidth 4 cm]{
            \includegraphics[width=2.5cm]{M1Figure-eps-converted-to}
        }
        \hfil
        \subfigure[\textwidth 4 cm]{
            \includegraphics[scale=.45]{M2Figure-eps-converted-to}
        }
    \end{center}
    \caption{
    The shapes on which we focus for the proof of Theorem \ref{M1Theorem}. 
    }
\label{fig-example}
\end{figure}

Observe that Theorem~\ref{DefectProbabilityTheorem} does not prove asymmetry of
a uniform attachment model. For this we would need to prove that the
{\it graph defect} $D(G)>0$ whp. However, we are able to make some statements
about symmetry/asymmetry of this model. We discuss it next.

In the case $m=1$, both the uniform and preferential attachment process yield
trees.  In such trees, we count the number of vertices that are parents of 
precisely two nodes, both leaves (which some authors have called a \emph{cherry}),
as illustrated in Figure~\ref{fig-example}(a).  The two children may be swapped,
thereby forming a symmetry.  We find, via a P\'{o}lya urn approach, that the
number of cherries is  positive with high probability, 
which gives symmetry
with high probability in both models.  Furthermore, as a result of the methods
that we apply, we are able to compute the asymptotic fraction of simple cherries,
which turns out to be approximately $4/105$ and $1/24$ in the preferential and uniform cases,
respectively (see Section \ref{M1TheoremProof}).

% Discuss Theorem 4!
The case $m=2$ is midway between the high-probability symmetry of the $m=1$
case and the conjectured asymmetry of the $m \geq 3$ case.  Examining the 
asymptotic probability of two vertices making the same choices and being
unchosen by subsequent vertices yields the following results as
shown in Figure~\ref{fig-example}(b).
\begin{theorem}[Symmetry results for $m=1,2$]
    \label{M1Theorem}
    \label{M2Theorem}
    Fix $m=1,2$, and let $G_n \distrib \UA(n, m)$ or $G_n \distrib \PA(n,m)$.
    Then there exists a constant $C > 0$ such that, for $n$ sufficiently large,
    \[
        \Pr[|\Aut(G_n)| > 1] > C.
    \]
    In the result for $m=1$, for both models, we can strengthen the statement  
    to symmetry with high probability (that is, the statement is true for all
    $C < 1$).
\end{theorem}

We conjecture that
% symmetry with high probability also holds in the preferential attachment
% model for $m=1$ and that, 
for $m=2$, in both models, 
$\Pr[|\Aut(G_n)| > 1]$ converges to a constant  strictly less than $1$.

The result for $m=2$ is particularly interesting in light of the fact that empirical
investigations of the symmetries of $\UA(n,2)$ graphs with insufficiently 
many samples (cf. Figure \ref{fig-sym}) may lead to the incorrect conclusion that there is asymmetry
with high probability in this case.

For fixed $m \geq 3$, we propose the following conjecture.
\begin{conjecture}[Asymmetry conjecture]
    \label{M3Conjecture}
    Fix $m\geq 3$ and let either $G_n \distrib \UA(n,m)$ or $G_n \distrib \PA(n,m)$.
    Then
    \[
        \Pr[|\Aut(G_n)| > 1] \xrightarrow{n\to\infty} 0.
    \]
    That is, graphs drawn according to the specified distributions are asymmetric
    with high probability.
\end{conjecture}
%\begin{figure}[h]
%\centerline{
         %\psfig{file=pref-1.ps,width=\basswidth}\hspace*{\gutter}
%         \psfig{file=pref-4.ps,width=\basswidth}}
        %\includegraphics[width=\basswidth]{pref-1.pdf}\hspace*{\gutter}
        %\includegraphics[width=\basswidth]{pref-4.pdf}
%}
%\medskip
%\centerline{\hbox to \basswidth{\hfill(a)\hfill}\hspace*{\gutter}
         %\hbox to \basswidth{\hfill(b)\hfill}}
%\caption{Logarithm of the number of automorphisms versus the number of 
%vertices for a preferential attachment graph with $m=1$ (on the left) 
%and $m=4$ (on the right).
%These experiments clearly indicate that such graphs are asymmetric for
%$m\geq 2$, however, proving it is a challenge.}
%\label{fig-sym}
%\end{figure}
% Empirical stuff.
\begin{figure}[h]
    \begin{center}
        %\includegraphics[scale=.6,clip,trim=2cm 7cm 1cm 6.5cm]{defect_swaps.pdf}
        %\includegraphics[scale=.55,clip,trim=2cm 7cm 1cm 6cm]{defect_randperm.pdf}
        %\includegraphics[width=\basswidth,clip,trim=2cm 7cm 1cm 6.5cm]{defect_swaps.pdf}\hspace*{\gutter}
        %\includegraphics[width=\basswidth,clip,trim=2cm 7cm 1cm 6cm]{defect_randperm.pdf}
        \includegraphics[width=\basswidth,clip]{defects}
    \end{center}
    \caption{
        Plots showing minimum/maximum vertex defects for certain classes of
        permutations.
    }
    \label{fig-sym}
\end{figure}

Empirical evidence in support of this conjecture abounds.  For instance, 
\cite{macarthur2006} give plots of number of automorphisms as $n$ increases for sampled
graphs, which show initial increase and then swift decay to $1$.  We contribute
defect-based evidence here.

Figure~\ref{fig-sym}  
shows growth of a graph defect estimate as $n$, the number of vertices
of the sampled graphs, grows large, for a few values of $m$.  As only a small 
subset of permutations could be sampled due to time and space constraints, the
pictured defect estimates only give upper bounds on the true defects.
For $m=1$, the estimate quickly drops to $0$, due to the presence of automorphisms
that are swaps of two vertices, as the proof of Theorem \ref{M1Theorem} indicates.
For $m=2$, the estimate grows away from $0$, but this does not give a complete
picture of the situation in this case: it fails to capture the 
phenomenon of symmetry with asymptotically nonzero (but quite small) probability 
predicted by Theorem~\ref{M2Theorem}.
For $m \geq 3$, the graph defect exhibits logarithmic growth, which is in keeping
with the statement of Theorem \ref{ExpectedDefectTheorem}.  Furthermore, since the
defect grows away from $0$, the evidence is in keeping with the asymmetry
conjecture (though the small permutation sample size prevents us from claiming
it as strong evidence of the conjecture).

We also give some weak supporting evidence in the form of a theorem about 
probability of automorphism group membership for sequences of permutations,
that of  
Theorem \ref{DefectProbabilityTheorem}.


Finally, we discuss the sliding window model, that could
naturally capture the behavior of dynamic networks in which new nodes are very
unlikely to attach to old ones, but whose attachment policy is otherwise quite
general. (For example, one may think of a social network whose nodes are people
admitted to a university and whose edges represent friendships formed after
admission, and  then, except with small probability, nodes will choose neighbors
in a window of bounded size.)
The next result deals with symmetry in the sliding window model.  If windows
are restricted to be of expected length less than a constant bound, then 
considering the event that nodes $n-1$ and $n$ form a cherry shows that
symmetry results with nonzero probability.  
%This can be strengthened in the
%case where window sizes are uniformly bounded above by a constant, by
%considering the event that a cherry forms in one of several non-overlapping
%subintervals of the vertex set.  The next result follows.
\begin{theorem}[Symmetry results for sliding window model]
    \label{SlidingWindowTheorem}
    In the sliding window model with random window size, for any $m$, if there 
    exists a constant $w$ such that $\E[W_i] \leq w$ for all $i$, then the probability of 
    symmetry is asymptotically positive.  If there exists $w$ such that, for 
    all $i$, $W_i \leq w$ with probability $1$, then a graph drawn according to
    this distribution is symmetric with high probability.
\end{theorem}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Proofs}
\label{Proofs}
In this section, we fix some useful notation, then give proofs of the theorems.
Given two vertices $u$ and $v$, we write $E[u,v]$ for the event that there is an
(undirected) edge between $u$ and $v$.
For vertex $u \in [n]$ and permutation $\pi$, we can write 
the defect $D_\pi(u)$ as:  
\[
    D_\pi(u) = \sum_{v=1}^n B_{u,\pi}(v),
\]
where we define $B_{u,\pi}(v)$ to be $1$ if $v \in \symmdiff$ and $0$ otherwise.
We can express each such indicator in terms of edge events:
\[
    B_{u,\pi}(v) 
    = (E[v,\pi(u)] \lintersect \neg E[\pi^{-1}(v), u])
    \lunion (\neg E[v,\pi(u)] \lintersect E[\pi^{-1}(v), u]).
\]
Note that the two conjunctions are disjoint.


\subsection{Proof of Theorem \ref{ExpectedDefectTheorem}}

We now assume uniform attachment model.
First, we state some useful lemmas about probabilities of edge events. 
We omit the simple proofs.
\begin{lemma}
    \label{EdgeProbMonotonicityLemma}
    For all $i,q,j,r$ such that $i < j$ and $q < r$, if $j < r$, then
        $\Pr[E[i,j]] > \Pr[E[q,r]]$.
\end{lemma}

\begin{lemma}
    \label{EdgeProbLowerBoundLemma}
    \label{EdgeProbUpperBoundLemma}
    For all $i < j$,
    \[
    \frac{1}{j} \leq \Pr[E[i,j]] \leq \frac{2m}{j}.
    \]
\end{lemma}

\begin{lemma}
    \label{ProbConjunctionRemoveNegationLemma}
    For all $i, q, j, r$ such that $i<j$, $q<r$, $r \geq 2m+1$, and either
    $i\neq q$ or $j\neq r$,
    \[
        \Pr[E[i,j]\lintersect \neg E[q,r]] \geq \Pr[E[i,j] \lintersect E[q,r]].
    \]
\end{lemma}


\begin{lemma}
    \label{EdgeProbSumUpperBoundLemma}
    For all $x \geq 1$,
        $\sum_{i=1}^x \Pr[E[i,x]] \leq 2m$.
\end{lemma}

\begin{lemma}[Harmonic Sum Log Sandwich] 
    \label{HarmonicSumLogSandwichLemma}
    For all $n$ and $j \in \Z$ such that $1\leq j \leq n$, 
    \[
    \log\frac{n}{j} \leq \sum_{i=j}^n \frac{1}{i} 
                        \leq \frac{1}j + \log\frac{n}{j}.
    \]
\end{lemma}

Now we move on to the proof of the main result.  Throughout, we
assume that $u < \pi(u)$; 
the case $u > \pi(u)$ follows from this by noting that
$D_{\pi^{-1}}(\pi(u))=D_\pi(u)$. 
%by an analogous proof
First, we derive the lower bound.  We start by lower bounding the probability of
event $B_{u,\pi}(i)$ by the probability of an edge.
For any vertex $i$ such that $\pi^{-1}(i)
\geq 2m+1$ and $i \neq u, \pi(u)$ (so all but a constant number of them), we have
\begin{align*}
    \Pr[B_{u,\pi}(i)] 
    &= \Pr[E[i,\pi(u)] \lintersect \neg E[\pi^{-1}(i), u]]
    + \Pr[\neg E[i, \pi(u)] \lintersect E[\pi^{-1}(i), u]] \\
    &\stackrel{(a)}{\geq} \Pr[E[i,\pi(u)] \lintersect E[\pi^{-1}(i),u]]
    + \Pr[E[\pi^{-1}(i), u] \lintersect \neg E[i, \pi(u)]] \\
    &= \Pr[E[\pi^{-1}(i),u]].
\end{align*}
Here, (a) is a result of Lemma \ref{ProbConjunctionRemoveNegationLemma} since
$\max\{\pi^{-1}(i), u\} \geq 2m+1$.
Hence, %provided $\pi(u) < \max\{\omega(\pi,u)+1, 2m+1\}$,
\begin{align*}
    \sum_{i=1}^n \Pr[B_{u,\pi}(i)] 
    &\geq \sum_{\pi^{-1}(i) = \max\{u+1, (2m+1)\}}^n \Pr[B_{u,\pi}(i)] \\
    &\stackrel{(a)}{\geq} \sum_{\pi^{-1}(u) \neq \pi^{-1}(i) = \max\{u+1, (2m+1)\}}^n
                         \Pr[E[\pi^{-1}(i), u]] \\ %+ \Pr[B_{u,\pi}(u)]\delta(\pi^{-1}(u) \geq 2m+1) \\
    &\stackrel{(b)}{\geq} \sum_{\pi^{-1}(u) \neq \pi^{-1}(i) = \max\{u+1, (2m+1)\}}^n
                         \frac{1}{\pi^{-1}(i)} \\
    &\stackrel{(c)}{\geq} \log\left( \frac{n}{\max\{u+2, (2m+2)\}} \right)
    = \log \left( \frac{n}{\max\{\omega(\pi, u)+2, 2m+2 \}} \right), 
\end{align*}
where (a) is a consequence of the previous inequality, (b) is an invocation of
Lemma \ref{EdgeProbLowerBoundLemma}, and (c) is a result of Lemma
\ref{HarmonicSumLogSandwichLemma} and the observation that, if
$\pi^{-1}(u) \geq \max\{u+1, 2m+1\}$,
then its contribution to the sum is  %lower bounded by 
$\frac{1}{\pi^{-1}(u)} \leq \frac{1}{\max\{u+1,2m+1)\}}$.
This completes the proof of the lower bound.

Now we prove the upper bound.  We start by upper bounding the probability of
$B_{u,\pi}(i)$: 
\begin{align*}
    \Pr[B_{u,\pi}(i)] 
    &= \Pr[E[i,\pi(u)] \lintersect \neg E[\pi^{-1}(i), u]]
    + \Pr[\neg E[i, \pi(u)] \lintersect E[\pi^{-1}(i), u]] \\
    &\stackrel{(a)}{\leq} \Pr[E[i,\pi(u)]] + \Pr[E[\pi^{-1}(i),u]] ,
\end{align*}
where (a) is a consequence of two applications of monotonicity of
probabilities. Hence
\begin{align*}
    \sum_{i=1}^n \Pr[B_{u,\pi}(i)]
    &\stackrel{(a)}{\leq} \sum_{i=1}^n \Pr[E[i,\pi(u)]]
    + \sum_{\pi^{-1}(i)=1}^n \Pr[E[\pi^{-1}(i),u]] \\
    &\stackrel{(b)}{\leq} 1 + 2\sum_{\pi^{-1}(i)=1}^n \Pr[E[\pi^{-1}(i),u]] \\
    &\leq 1 + 2\sum_{\pi^{-1}(i)=1}^{u} \Pr[E[\pi^{-1}(i),u]]
    + 2 \sum_{\pi^{-1}(i)=u}^{n} \Pr[E[\pi^{-1}(i),u]] \\
    &\stackrel{(c)}{\leq} 1 + 4m 
      + 2\sum_{\pi^{-1}(i)=u}^n \Pr[E[\pi^{-1}(i),u]],
\end{align*}
where (a) follows from the previous inequality %, (b) from Lemma 
%\ref{EdgeProbMonotonicityLemma}, 
and (c) from Lemma 
\ref{EdgeProbSumUpperBoundLemma}.  The justification for (b) is slightly more
complicated than for the other two steps: it follows from the fact that %for any $i \neq u$,
\[
    \Pr[E[i,\pi(u)]] \leq \Pr[E[i,u]], \ \ \ \ i \neq u ,
\]
which can be seen as follows: for $u\neq i < \pi(u)$, it follows from Lemma
\ref{EdgeProbMonotonicityLemma}.  If $i=\pi(u)$, then the left-hand side is
$0$, so the inequality holds.  Finally, if $i > \pi(u) > u$, then the two
probabilities are equal, due to the uniformity of the attachment process.
For the case $i=u$, the inequality fails, and we instead upper bound that
term by $1$.

Thus, we can upper bound some more:
\begin{align*}
    \sum_{\pi^{-1}(i)=u}^n \Pr[E[\pi^{-1}(i),u]]
    \stackrel{(a)}{\leq} 2m \sum_{\pi^{-1}(i)=u}^n \frac{1}{\pi^{-1}(i)}
    \stackrel{(b)}{\leq} 2m\left(1  + \log\frac{n}{u}\right),
\end{align*}
where (a) follows from Lemma \ref{EdgeProbUpperBoundLemma} and (b) from Lemma 
\ref{HarmonicSumLogSandwichLemma}.  This completes the proof.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Proof of Theorem \ref{DefectProbabilityTheorem}}   
We will start by splitting into three cases: $u_n = O(1)$,
$u_n \to \infty$, or neither is true (that is, $\limsup u_n = \infty$ and
$\liminf u_n = O(1)$).  The first we will handle using the second moment
method.  For the second, we use various upper bounds.  The third can be handled
by splitting the sequence into two subsequences, each of which can be handled via
the previous two arguments.

% u_n = O(1). Second moment method.
\subsubsection{$u_n = O(1)$}
In the case where $u_n = O(1)$, applying Theorem \ref{ExpectedDefectTheorem}
shows that $\E[D_{\pi_n}(u_n)] = \Theta(\log n)$,
so that it grows to infinity.  Thus, we will use the second moment method
to show that $\Pr[D_{\pi_n}(u_n) = 0] \to 0$.  We start with
\[
    \Pr[D_{\pi_n}(u_n) = 0] 
    \leq \frac{\Var[D_{\pi_n}(u_n)]}{(\E[D_{\pi_n}(u_n)])^2}.
\]
Recall that we can write $D_{\pi_n}(u_n)$ as a sum of indicators, so
that the numerator becomes
\begin{align*}
    &\Var[D_{\pi_n}(u_n)] 
    = \Var\left[\sum_{j=1}^n B_{u_n,\pi_n}(j)\right] \\
    &= \sum_{j=1}^n \Var[B_{u_n,\pi_n}(j)] 
      + 2\sum_{1\leq i < j \leq n} \Cov[B_{u_n,\pi_n}(i), B_{u_n,\pi_n}(j)] \\
    &\stackrel{(a)}{\leq} \sum_{j=1}^n \E[B_{u_n,\pi_n}(j)]
      + 2\sum_{1\leq i < j \leq n} \Cov[B_{u_n,\pi_n}(i), B_{u_n,\pi_n}(j)] \\
    &\stackrel{(b)}{\leq} \E[D_{\pi_n}(u_n)]  
      + 2\sum_{1\leq i < j \leq n} \Cov[B_{u_n,\pi_n}(i), B_{u_n,\pi_n}(j)],
\end{align*}
where (a) is because the value of an indicator function is 
either $0$ or $1$, and (b) is by linearity of expectation.
%\[
%    \sum_{j=1}^n \E[B_{u_n,\pi_n}(j)]^2
%    = \sum_{j=1}^n \E[B_{u_n,\pi_n}(j)]
%    = \E\left[\sum_{j=1}^n B_{u_n,\pi_n}(j)\right]
%    = \E[D_{\pi_n}(u_n)].
%\]
We show that the sum of covariance
terms is small enough leading to $\Var[D_{\pi_n}(u_n)]=O(\E[D_{\pi_n}(u_n)])$.
The following inequality will be useful for arbitrary $h$ and $k$:
\begin{align}
    \Cov[B_{u_n,\pi_n}(h), B_{u_n,\pi_n}(k)] 
    &= \Pr[B_{u_n,\pi_n}(h) \lintersect B_{u_n,\pi_n}(k)] 
      - \Pr[B_{u_n,\pi_n}(h)]\Pr[B_{u_n,\pi_n}(k)] \\
   &\leq \Pr[B_{u_n,\pi_n}(h) \lintersect B_{u_n,\pi_n}(k)] \\
   &\leq \Pr[B_{u_n,\pi_n}(h)]. \label{CovarianceLessThanProbInequality}
\end{align}

First, we consider a few subsets of terms for which elementary probability
inequalities suffice.  After dealing with these subsets, we will be left
with a subset such that the ordering of vertices involved in the relevant
edge events is sufficiently constrained for us to bound the term contributions
using edge independence and dependence quantification properties.

Consider the contribution of pairs $i < j$ such that  at least one of 
$\pi_n^{-1}(i)$, $\pi_n^{-1}(j)$ is at most $u_n$.  It is bounded above by
\begin{align*}
    &\sum_{\pi_n^{-1}(i) = 1}^{u_n} \sum_{j=1}^n \max\{ \Cov[B_{u_n,\pi_n}(i),B_{u_n,\pi_n}(j)], 0\} \\ %\logicor 0
    &+ \sum_{\pi_n^{-1}(j)=1}^{u_n} \sum_{i=1}^n \max\{ \Cov[B_{u_n,\pi_n}(i), B_{u_n,\pi_n}(j)], 0\}\\ %\logicor 0 \\
    &\leq \sum_{\pi^{-1}(i)=1}^{u_n} \sum_{j=1}^n \Pr[B_{u_n,\pi_n}(j)]
          + \sum_{\pi^{-1}(j)=1}^{u_n} \sum_{i=1}^n \Pr[B_{u_n,\pi_n}(i)] \\
    &= 2 \sum_{i=1}^{u_n} \E[D_{\pi_n}(u_n)]
    = 2u_n \E[D_{\pi_n}(u_n)] = O(\E[D_{\pi_n}(u_n)]).
\end{align*}
Here, the first upper bound is by considering all possible values for 
$\pi_n^{-1}(i) \leq u_n$ and $\pi_n^{-1}(j) \leq u_n$, respectively.  For each such
value, the other element of the pair may take values in a (possibly strict) 
subset of $[n]$.  The upper bound results from summing over all values in $[n]$
of the second parameter and adding only non-negative terms.  The second upper
bound is an application of (\ref{CovarianceLessThanProbInequality}).  The
rest follows from linearity of expectation.  In the same way, we get an
analogous bound for the case where either $\pi_n^{-1}(i)$ or $\pi_n^{-1}(j)$
is equal to $\pi_n(u_n)$ and for the case where $i=u_n$.

Now, consider the contribution of pairs $i < j$ such that $i$ and $j$ are 
neighbors in the disjoint cycle representation of $\pi_n$ and such that
both $\pi_n^{-1}(i)$ and $\pi_n^{-1}(j)$ are at least $u_n + 1$.  The
contribution of such terms is at most
\begin{align*}
    \sum_{i=1}^n \max\{ \Cov[B_{u_n,\pi_n}(i), B_{u_n,\pi_n}(\pi_n(i))], 0 \} %\logicor 0
    \leq \sum_{i=1}^n \Pr[B_{u_n,\pi_n}(i)]
    \leq \E[D_{\pi_n}(u_n)].
\end{align*}

We next consider the case in which at least one of $i, j \geq \pi_n(u_n)$
and $i$ and $j$ are not neighbors in the disjoint cycle representation of
$\pi_n$.  When $i=\pi_n(u_n)$, $B_{u_n,\pi_n}(i) = 0$ with probability $1$,
so that such covariance terms are $0$.  The same holds for $j=\pi_n(u_n)$.
Thus, we are left with the case in which at least one of $i, j > \pi_n(u_n)$.
In this case, the covariance is $0$, as a simple consequence of edge
independence properties.  In particular, we claim that $B_{u_n,\pi_n}(i)$
and $B_{u_n,\pi_n}(j)$ are independent in this situation.  We have one of
\begin{align}
    \label{eqn:1}
    u_n < \pi_n(u_n) < i < j
\end{align}
or
\begin{align}
    u_n < i < \pi_n(u_n) < j.
\end{align}    
For simplicity, we only explain (\ref{eqn:1}).  The other case is similar.

Both $\pi_n^{-1}(i)$ and $\pi_n^{-1}(j)$ are greater than $u_n$
(this from the fact that $u_n$ is the least non-fixed vertex, and both
$i$ and $j$ are greater than $u_n$).  Thus, the variable
$B_{u_n,\pi_n}(i)$ involves only the choices at times 
$i$ and $\pi_n^{-1}(i)$,
and $B_{u_n,\pi_n}(j)$ involves only the choices at times 
 $j$ and $\pi_n^{-1}(j)$.
By the assumption that $i$ and $j$ aren't cycle neighbors, these two sets
of choices are disjoint, so that they are independent, so that the covariance
is $0$, as claimed.

With the easy cases out of the way,  we can focus on the contribution of
pairs such that $\pi_n^{-1}(i), \pi_n^{-1}(j) > u_n$, $i$ and $j$ are not
neighbors in the disjoint cycle representation of $\pi_n$,  
$i, j < \pi_n(u_n)$, and $\pi_n^{-1}(i) \neq \pi_n(u_n) \neq \pi_n^{-1}(j)$. 
We proceed by expressing an arbitrary covariance term in terms of edge events.
\begin{align*}
    \Cov[B_{u_n,\pi_n}(i), B_{u_n,\pi_n}(j)]
    = \Delta_{1,1}(i,j) + \Delta_{1,2}(i,j) + \Delta_{2,1}(i,j) + \Delta_{2,2}(i,j),
\end{align*}
where
\begin{align*}
    &\Delta_{k,l}(i,j) = \Pr[F_k(i) \lintersect F_l(j)] - \Pr[F_k(i)]\Pr[F_l(j)],
\end{align*}
where we define
\begin{align*}
    &F_1(x) = E[x,\pi_n(u_n)] \lintersect \neg E[\pi_n^{-1}(x), u_n] \\
    &F_2(x) = \neg E[x, \pi_n(u_n)] \lintersect E[\pi_n^{-1}(x), u_n].
\end{align*}
%%%%%%%%%%%%%%%%
% post-acceptance: introduce more compact notation here.
To write these more succinctly and to simplify a later definition, we define
\begin{align*}
    \rho_1(k)
    &= \begin{cases}
        0   &  k=1\\
        -1  &  k=2,
      \end{cases} \\
    \rho_2(k)
    &= \begin{cases}
        1   &  k=1  \\
        0   &  k=2
      \end{cases}.
\end{align*}
Then define $\rho_1'(k) = -(\rho_1(k) + 1)$ and $\rho_2'(k) = -\rho_2(k)+1$.
With this notation, we can write each $F_k$ as
\[
    F_k(x) = E[\pi_n^{\rho_1(k)}(x), \pi_n^{\rho_2(k)}(u_n)] 
             \lintersect \neg E[\pi_n^{\rho_1'(k)}(x), \pi_n^{\rho_2'(k)}(u_n)].
\]
%%%%%%%%%%%%%%%

To proceed, we need some lemmas about probabilities of conjunctions and
negations of edge events. 
We again omit the simple proofs. 

\begin{lemma}[Edge Event Probabilities]
    \label{EdgeEventProbLemma}
    For all $i, q < j$, $i\neq q$,
    \begin{align*}
        &\Pr[E[i,j]] = 1 - \left( \frac{j-2}{j-1} \right)^m \\
        &\Pr[E[i,j] \lintersect \neg E[q,j]] 
          = \left( 1 - \left(\frac{j-3}{j-2} \right)^m \right) \left( \frac{j-2}{j-1} \right)^m \\
        &\Pr[E[i,j] \lintersect \neg E[q,j]] = \Pr[E[i,j]]\Pr[\neg E[q,j]] (1 + O(j^{-1}))  \\
        & \Pr[\neg E[i,j] \lintersect \neg E[q,j]] = \Pr[\neg E[i,j]]\Pr[\neg E[q,j]] (1 + O(j^{-2})).
    \end{align*}
\end{lemma}

\begin{lemma}[Edge Negation Inequality]
    \label{EdgeNegationInequalityLemma}
    For $j \geq 2m+1$,
    \[
        \Pr[\neg E[i,j]] \geq \Pr[E[i,j]].
    \]
\end{lemma}

\begin{lemma}[Edge Conjunction Inequalities]
    \label{EdgeConjunctionInequalityLemma}
    For all $i, q, j, r$ such that $i < j$ and $q < r$,
    and either $i \neq q$ or $j \neq r$, 
    \[
        \Pr[E[i,j] \lintersect E[q,r]] \leq \Pr[E[i,j]]\Pr[E[q,r]]
    \]
    and
    \[
        \Pr[E[i,j] \lintersect \neg E[q,r]] \geq \Pr[E[i,j]]\Pr[\neg E[q,r]].
    \]
\end{lemma}

\begin{lemma}
    For all $i,q,j,r$ such that $i < j$, $q < r$, $r \geq 2m+1$, 
    and either $i \neq q$ or $j \neq r$,
    \[
        \Pr[E[i,j] \lintersect \neg E[q,r]] 
        \geq \Pr[E[i,j] \lintersect E[q,r]].
    \]
\end{lemma}

Now, the probability of each $F_k(x)$ can be lower bounded by the product of the
involved edge probabilities, by Lemma \ref{EdgeConjunctionInequalityLemma},
which gives an upper bound on each $\Delta_{k,l}(i,j)$.  We call the resulting
expression $\Delta'_{k,l}(i,j)$;
%%%%%%%%%%%%%%%%%%%
% Post acceptance revision: explicit definition of Delta'.
more specifically, $\Delta'_{k,l}(i,j)$ is given by
\begin{align*}
    \Delta'_{k,l}(i,j) 
    = &\Pr[F_k(i) \lintersect F_l(j)]     \\
      &- \Pr[E[\pi_n^{\rho_1(k)}(i), \pi_n^{\rho_2(k)}(u_n)]] 
        \Pr[\neg E[\pi_n^{\rho_1'(k)}(i), \pi_n^{\rho_2'(k)}(u_n)]] \\
      & \cdot \Pr[E[\pi_n^{\rho_1(l)}(j), \pi_n^{\rho_2(l)}(u_n)]]
        \Pr[\neg E[\pi_n^{\rho_1'(l)}(j), \pi_n^{\rho_2'(l)}(u_n)]]
\end{align*}
%%%%%%%%%%%%%%%%%%%
Now we show that $\Delta'_{1,1}(i,j) \leq 0$.
\begin{align*}
    \Pr[F_1(i) \lintersect F_1(j)] 
    &\stackrel{(a)}{=} \Pr[E[i,\pi_n(u_n)] \lintersect E[j,\pi_n(u_n)]] 
      \cdot \Pr[\neg E[\pi_n^{-1}(i),u_n] \lintersect \neg E[\pi_n^{-1}(j),u_n]] \\
    &\stackrel{(b)}{\leq} \Pr[E[i,\pi_n(u_n)]] \Pr[E[j,\pi_n(u_n)]]  \Pr[\neg E[\pi_n^{-1}(i),u_n]] \Pr[\neg E[\pi_n^{-1}(j),u_n]].
\end{align*}
Here, (a) is an application of edge independence properties: we have 
$i < j < \pi_n(u_n)$ and $u_n < \pi_n^{-1}(i), \pi_n^{-1}(j)$, and neither of
$\pi_n^{-1}(i), \pi_n^{-1}(j)$ is equal to $\pi_n(u_n)$.  The inequality,
(b), is due to applying edge independence to split the negated events
and applying Lemma \ref{EdgeConjunctionInequalityLemma} to split
the other conjunction.  It follows from this inequality that
$\Delta'_{1,1}(i,j) \leq 0$.

In a similar manner, we handle $\Delta'_{1,2}(i,j)$: 
\begin{align*}
    \Delta'_{1,2}(i,j)
    &= \Pr[E[i,\pi_n(u_n)] \lintersect \neg E[j,\pi_n(u_n)]]\Pr[\neg E[\pi_n^{-1}(i),u_n]]\Pr[E[\pi_n^{-1}(j),u_n]] \\
      &- \Pr[E[i,\pi_n(u_n)]]\Pr[\neg E[j,\pi_n(u_n)]]\Pr[\neg E[\pi_n^{-1}(i),u_n]] \Pr[E[\pi_n^{-1}(j),u_n]] \\ 
    &\leq \Pr[E[i,\pi_n(u_n)] \lintersect \neg E[j,\pi_n(u_n)]] - \Pr[E[i,\pi_n(u_n)]]\Pr[\neg E[j,\pi_n(u_n)]] \\
    &= \Pr[E[i,\pi_n(u_n)]]\Pr[\neg E[j,\pi_n(u_n)]](1 + O(\pi_n(u_n)^{-1}) - 1) \\
&\leq 
\frac{2m}{\pi_n(u_n)}O(\pi_n(u_n)^{-1}) 
    = O\left(\frac{1}{(\pi_n(u_n))^2}\right).
\end{align*}
The first equality is by applying edge independence properties.  The 
inequality is by factoring out the probabilities involving $u_n$ and upper 
bounding them by $1$.  The final two steps are by applying Lemmas 
\ref{EdgeEventProbLemma} and \ref{EdgeProbUpperBoundLemma}.

The other cases are handled analogously.  Thus, each covariance term
in the set considered is upper bounded by $\frac{C}{\pi_n(u_n)^2}$, where
$C$ is a constant.  Since there are at most $C'\pi_n(u_n)^2$ such pairs,
the total contribution is at most a constant.
This completes the proof for $u_n = O(1)$.

\subsubsection{$u_n \to \infty$}  
% u_n \to \infty.
For $u_n \to \infty$, we will upper bound the probability of the event
\[
    A = \intersect_{i=1}^{u_n-1} [B_{u_n,\pi_n}(i)=0],
\]
which is a superset of $[D_{\pi_n}(u_n)=0]$.  The condition that $u_n \to \infty$
gives us enough fixed vertices for a somewhat simple proof.
Observe that the event is equivalent to $u_n$ and $\pi_n(u_n)$ having
precisely the same neighbors in $[u_n-1]$.

First, condition on the set $N$ of distinct choices made by vertex 
$\pi_n(u_n)$ and
let $N_1 = N \lintersect [u_n-1]$.  Then
\[
    |N_1| \leq |N| \leq m.
\]
Now, if $A$ occurs, then all $m$ of the choices $c_{u_{n},j}$ at time 
$u_n$ lie in 
$N_1$.  This happens with probability at most
\[
    \left( \frac{|N_1|}{u_n - 1} \right)^m 
    \leq \left( \frac{m}{u_n-1} \right)^m 
    = O(u_n^{-m}).
\]
Since $u_n \to \infty$, this tends to $0$, which completes the proof
for this case.

% Third case.
\subsubsection{$u_n$ is neither bounded nor tending to $\infty$}
Our goal is to show that, for any $\epsilon > 0$, there exists $N$ such that,
for all $n \geq N$,
\[
    \Pr[D_{\pi_n}(u_n) = 0] < \epsilon.
\]
In the case where $u_n$ is neither bounded nor tending to $\infty$, we start
by choosing an appropriate $C=C(\epsilon)$ and partitioning the sequence of
permutations into two subsequences: those for which $u_n \leq C$ and those
for which $u_n > C$.  

For the former subsequence (call its sequence of probabilities $a_n$), 
we can apply the result for $u_n = O(1)$ to conclude that there is some 
$N$ such that, if $n \geq N$, then $a_n < \epsilon$.
For the latter (call its sequence of probabilities $b_n$), we can apply
the argument used in the proof for $u_n \to \infty$ to conclude that 
there is a large enough $C(\epsilon)$ such that $b_n < \epsilon$ 
for all $n$.
This completes the proof of Theorem \ref{DefectProbabilityTheorem}.




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Proof of Theorem \ref{M1Theorem}}
\label{M1TheoremProof}
{\sc Case $m=1$}.\\
To prove symmetry with high probability, we give a positive lower bound on the
asymptotic number of cherries which holds with high probability.  
    This we do by formulating
    the preferential and uniform attachment processes as urn models, in which one type
    of ball corresponds to a \emph{simple} cherry (i.e., a node with exactly two children,
    both leaves).  We then use Theorem 3.21 of \cite{Janson2004} 
    (see also \cite[Section V.9.3]{AthreyaNey1972}) to
    determine the asymptotic fraction of such balls, which turns out to be positive.

    %%%%%%%%%%%%%%%%%%%%%%%
    % Post-acceptance: Explain Theorem 3.21 of Janson!
    We now review the aforementioned Theorem 3.21.  In the general setting, we have an urn model
    with
    $q$ types of balls, and for each non-negative integer time $t$, we have a (random)
    vector of ball type counts: $X_t = (X_{t,1}, \dots, X_{t,q})$, where $\{X_t\}_{t=0}^\infty$
    forms a Markov chain.  Furthermore, each ball type $i$ gets a fixed weight (also called 
    \emph{activity}) $a_i$ as well as a random $q$-dimensional vector $\xi_{i} = (\xi_{i,j})_{j=1}^q$ which will be
    used to form the transition matrix.  At time $t$, a ball is drawn with probability proportional
    to its weight and current count:
    \[
        \Pr[\text{Type $j$ is drawn at time $t$}] = \frac{a_jX_{t-1, j}}{\sum_{k=1}^q a_kX_{t-1,k}}.
    \]
    Next, the ball is returned to the urn, and a vector $w_t=(w_{t,r})_{r=1}^q$ is drawn according to the distribution
    of $\xi_j$ (i.e., for any $r$ and $x$, $\Pr[w_{t,r} = x] = \Pr[\xi_{j,r} = x]$),
    independent of everything else.  Finally, for each $k\in[q]$, $w_{t,k}$ balls of type
    $k$ are added to the urn.  This results in $X_t$.  The main object which is defined in order
    to study the asymptotic behavior of the number of balls of each type is the $q\times q$ matrix $A$, 
    whose entries are given by
    \[
        A_{i,j} = a_j\E[\xi_{j,i}].
    \]
    We call $A$ the \emph{transition matrix} of the model.  For us, the types of balls will correspond
    to different subtrees, and, roughly speaking, the weights will come from the degrees of the vertices
    in the subtrees (which is how we will encode the preferential attachment process).

    Then Theorem 3.21 states that, assuming several hypotheses and
    given that there are always balls with nonzero weight left, we have the following
    asymptotic result:
    \[
        \frac{X_t}{t} \xrightarrow{a.s.} \lambda_1 v_1,
    \]
    where $\lambda_1$ and $v_1$ are the principal eigenpair of $A$.  From this, we can read
    off the asymptotic linear growth of the number of any of the types of balls.

    Conveniently, the paper gives Lemma 2.1, which states sufficient conditions
    for the hypotheses of Theorem 3.21 to hold: we need only check that
    \begin{itemize}
        \item
            $A$ is \emph{irreducible}; that is, we define a partial order $i \succ j$,
            where $i \succ j$ if and only if there exists some $n \geq 0$ for which
            $(A^{n})_{j,i} > 0$ (that is, starting with ball of type $i$, it is possible
            for a ball of type $j$ to appear in the future).  Then we say that $A$ is
            irreducible if, for all $i$ and $j$, we have both $i \succ j$ and $j \succ i$.
        \item
            Almost surely, for all $j \neq i$,
            \begin{align*}
                \xi_{i,j} \geq 0,   && \xi_{i,i} \geq -1.
            \end{align*}
        \item
            For all $i$ and $j$,
            \[
                \E[\xi^2_{i,j}] < \infty.
            \]
        \item
            We have, for every $i$,
            \[
                \sum_{j} \E[\xi_{i,j}] \geq 0
            \]
            and, for some $i$,
            \[
                \sum_{j} \E[\xi_{i,j}] > 0.
            \]
    \end{itemize}
    %%%%%%%%%%%%%%%%%%%%%%%

    We focus first on the preferential 
    case, then explain how the proof changes for uniform attachment.

    For preferential attachment, we have the following types of balls:
    \begin{itemize}
        \item
            $SC$ (simple cherry): a node with precisely two children, both of whom are
            leaves.  This is modeled by a single ball of type $SC$. 
        \item
            $PC$ (protocherry): a node with precisely one child, which is a leaf.
            This is modeled by a single ball of type $PC$.
        \item
            $L$ (leaf): a leaf which is not part of a simple cherry or a protocherry.  
            This is modeled by a single ball of type $L$.
        \item
            $N$ (none of the above): a node with three or more children or with grandchildren.
            A single such node with degree $k$ is modeled by $k$ balls of type $N$.
    \end{itemize}
    Since these ball types will also correspond to coordinates in entries of a matrix, it
    will be convenient to associate $SC$ with $1$, $PC$ with $2$, $L$ with $3$, and $N$ with
    $4$.
    % Talk about the weights.
    With each type of ball, there is an associated weight, which comes from considering
    the number of half edges in each situation (each edge in the subtree corresponding to
    a given ball counts as two half edges; finally, there is a single half edge which comes
    from the root vertex's connection to its parent):
    \begin{align*}
        a_{SC}= 5   &&  a_{PC}=3    &&   a_{L}=1        &&   a_{N}=1.
    \end{align*}
    We can then formulate the process as follows.  We start with a tree with $4$ nodes,
    in order to avoid complications arising from the root.  Each such tree corresponds
    to a well defined initial vector of ball counts.
    At a given time step, we pick a ball randomly, where the probability of each type is in proportion to
    the current number of balls of that type and its weight: if we denote the type of the
    $j$th choice by $T_j$ and the number of balls of type $x$ at time $j$ by $N_j(x)$,
    \[
        \Pr[T_j = x] = \frac{a_xN_j(x)}{\sum_{y} a_y N_j(y)},
    \]
    where the sum is over the types.  After drawing a ball, we discard it and add new
    balls to the urn in the following manner, based on $T_j$.
    \begin{itemize}
        \item
            \begin{align*}
                SC
                \implies
                \begin{cases}
                    4N + 3L                 &   \text{with probability } 3/5 \\
                    3N + 1L + 1 PC          &   \text{with probability } 2/5  
                \end{cases}
            \end{align*}
        \item
            \begin{align*}
                PC
                \implies
                \begin{cases}
                    1SC                     &   \text{with probability } 2/3 \\
                    2N + 1PC                &   \text{with probability } 1/3
                \end{cases}
            \end{align*}    
        \item
            \[
                L   \implies        1PC
            \]    
        \item
            \[
                N   \implies    1L + 2N.
            \]    
    \end{itemize}
    %%%%%%%%%%%%%%%%%%%%%%%%%%
    % Post-acceptance: explain transitions in further detail.
    The transitions for $SC$ can be seen as follows.  Drawing a ball of type $SC$ means, in
    terms of the preferential attachment process, that
    either the root node of a simple cherry or one of its two children is chosen by the new
    vertex.  The root node has degree $3$, and each child has degree $1$, so that the total
    degree of the subgraph is $5$.  Thus, with probability $3/5$, the root node is chosen,
    which destroys the simple cherry.  It is replaced with $4$ balls of type $N$ (corresponding
    to a non-simple cherry whose root node has degree $4$), along with $3$ balls of type
    $L$ (corresponding to the three leaves).  With probability $2/5$, one of the two leaves
    of the simple cherry is chosen.  This destroys the simple cherry, replacing it with three
    balls of type $N$ (because the root of the simple cherry still has degree $3$), and the chosen child
    becomes a protocherry, which adds a single ball of type $PC$.  The other child becomes
    a leaf, which adds a ball of type $L$.

    The other transitions follow by similar reasoning.  Note that, in each case, the degree
    of each node in the new subtree must somehow be encoded in the number and types of balls.

    This yields, in the language of \cite{Janson2004}, the following transition matrix:
    \begin{align*} \newcommand\+{\phantom{-}}
        A = 
        \begin{pmatrix}
            -5  &  \+2     &  \+0       &  \+0 \\
            \+2   &  \+2    &  \+1       &  \+0 \\
            \+11  &  \+0     &    -1       &  \+1 \\
            \+18  &  \+2     &  \+0       &  \+1
        \end{pmatrix}.
    \end{align*}
    We demonstrate how $A$ was obtained from the transitions and the weights by explicitly
    computing the first column, which corresponds to the balls of type $SC$.  
    We need the vector $\E[\xi_{1}]$, which gives the expected number of balls of each type
    which are added to the urn after $SC$ is drawn (and before it is discarded).  Since
    $SC$ is discarded with probability $1$, we have $\E[\xi_{1,1}] = -1$.  The expected number
    of $PC$ balls added is $\E[\xi_{1,2}] = 0\cdot \frac{3}{5} + 1 \cdot \frac{2}{5} = 2/5$.
    For $L$, it is $\E[\xi_{1,3}] = 3\cdot \frac{3}{5} + 1 \cdot \frac{2}{5} = 11/5$.  Finally,
    for $N$, it is $\E[\xi_{1,4}] = 4\cdot\frac{3}{5} + 3\cdot\frac{2}{5} = 18/5$.  Multiplying
    by the weight $a_{SC}$ for simple cherries, we get the claimed first column of $A$.
    The other columns are computed analogously.
    %%%%%%%%%%%%%%%%%%%%%%%%%%
    Now, we can apply Lemma 2.1 of \cite{Janson2004} to show that the
	hypotheses of Theorem 3.21 there
are satisfied:
    % Irreducible.
    the process is easily seen to be irreducible, since, with positive probability, $SC$ yields
    $N$, which yields $L$, which yields $PC$, which yields $SC$.  The other hypotheses of Lemma 2.1
    are similarly trivial to check.
    From this, we conclude that all of the 
    hypotheses of Theorem 3.21, in addition to impossibility of essential extinction, 
    hold.  Thus, it remains to determine the dominant eigenvalue,
i.e., the largest real eigenvalue, 
and the corresponding eigenvector of $A$.
We get, from standard calculations, that the largest real eigenvalue of
$A$ is $\lambda_1 = 2$
(the other eigenvalues are $-1, -3, -5$), and that, after normalization, the corresponding eigenvector
    is 
    \[
        v_1^T 
=\tfrac1{105}(2,7,24,50)
\approx (0.019048, 0.06667, 0.22857, 0.47619).
    \]
Multiplying by $\lambda_1$, we get 
from \cite[Theorem 3.12]{Janson2004} 
that, asymptotically, 
the number of simple cherries
is with high probability 
approximately $\frac{4n}{105}$, which is certainly greater than $0$.  
    Thus, we have symmetry with high probability.

A similar proof (with the same types of balls, but with a slightly different interpretation
$N$, to be explained below)
yields the same result (with different constants) for the uniform 
attachment model.
%Post-acceptance: Explain uniform attachment modification better.
    Because the attachment process pays attention to number of nodes
    in a subgraph, rather than to degrees of vertices, a ball of
    type $N$ still corresponds to a root node with three or more children
    or with grandchildren, but now a single such node corresponds to
    a single ball of type $N$.  Its descendants are represented by
    other balls.

    The weights change, again to account for the number of vertices
    in each type of subtree.  Thus, for example, the weight of $SC$ is $3$.
    In this case, the weights of the balls are $(3, 2, 1, 1)$.

    Finally, the transitions change.  Namely, instead of adding multiple
    copies of $N$ in any given transition, we only add one.
%%%%%%%%%%%%%%%%%%%%%%%%%
%In this case,  
%the weights of the balls are $(3,2,1,1)$ and
Thus,
the transition matrix is 
    \begin{align*}\newcommand\+{\phantom{-}}
        A = 
        \begin{pmatrix}
            -3   &  \+1     &  \+0       &  \+0 \\
            \+2  &   -1     &  \+1       &  \+0 \\
            \+5  &  \+0     &   -1       &  \+1 \\
            \+3  &  \+1     &  \+0       &  \+0
        \end{pmatrix}
    \end{align*}
with dominant eigenvalue $1$ and corresponding normalized eigenvector
$%    \[
        v_1^T 
=\tfrac1{24}(1,4,6,7)$.
%    \]

%\subsection{Proof of Theorem \ref{M2Theorem}}
\med{\sc Case $m=2$}\\
We will show that, with positive probability, in both models, there
is at least one \emph{diamond} (i.e., a pair of nodes that choose the same
parents and that are not chosen by any subsequent nodes) as shown in
Figure~\ref{fig-example}(b).  The details are
technically more intricate than in the $m=1$ case, and the argument there does
not work in this case, because node $n$ must choose, from a set of size 
$\Theta(n^2)$ (pairs of vertices), one of $O(n)$ elements (previously chosen
pairs).  We thus rely on a birthday paradox-style argument to show that there
is a positive probability of two vertices making the same choices, then 
condition on the lexicographically smallest such pair to complete the proof.

Let $A(u,v)$ be the event that vertices $u$ and $v$ choose the same pair
of parents,
and let $B(u,v)$ be the event that $u$ and $v$ are both unchosen.  Now,
define $N(k)$ to be the number of pairs $u,v$ of vertices such that $k < u < v$
and $A(u,v)$ and $B(u,v)$ simultaneously hold.  Define $N_A(k)$ and $N_B(k)$
analogously for pairs for which events $A$ and $B$ hold, respectively.
Finally, denote by $S_{> x}$ the set $\{k \in [n] | k > x\}$.
We then aim to prove that
$\Pr[N(0) > 0] > C > 0$
for some constant $C$ and $n$ large enough.  We do this by proving
\[
    \Pr[N(n/2) > 0] > C > 0,
\]
in light of the fact that
\[
    \Pr[N(n/2) > 0] \leq \Pr[N(0) > 0].
\]
For any $x$, we have
\[
    \Pr[N(x) > 0]
    = \Pr[N(x) > 0 \lintersect N_A(x) > 0]
    = \Pr[N(x) > 0 | N_A(x) > 0] \cdot \Pr[N_A(x) > 0],
\]
where the first equality is from the fact that $[N(x) > 0] \subseteq [N(x)_A > 0]$.
The goal now is bound the remaining probabilities below by positive constants.  We
do this in the next two lemmas, which hold for both uniform and preferential
attachment graphs.  We will prove them in the uniform case, then explain the
modifications needed to extend them to the preferential case.

\begin{lemma}[Probability of two vertices picking the same pair]
    There exists a positive constant $C$ such that
    \[
        \Pr[N_A(n/2) > 0] > C
    \]
    for all $n$ sufficiently large.
\end{lemma}
\begin{proof}
    To show this, we will instead compute $\Pr[N_A(n/2) = 0]$ and bound it
    above by a constant less than $1$.  The condition $N_A(n/2)=0$ means
    that all vertices $> n/2$ choose distinct pairs.  This is given by
    % Post-acceptance correction: Replaced k-1 equality with leq.
    \begin{align*}
        \Pr[N_A(n/2)=0]
        &\leq \prod_{k=1}^{n/2} \left(1 - \frac{k-1}{(\frac{n}{2}+k-1)^2} \right) \\
        &\leq \prod_{k=1}^{n/2} \left( 1-\frac{k-1}{n^2} \right)
        \leq \prod_{k=n/4+1}^{n/2} \left( 1 - \frac{k-1}{n^2}  \right) \\
        &\leq \prod_{k=n/4+1}^{n/2} \left(1 - \frac{n}{4n^2} \right) 
        = \left(1 - \frac{(1/4)}{n}\right)^\frac{n}{4} 
        \xrightarrow{n\to\infty} e^{-\frac{1}{16}} < 1.
    \end{align*}
    %%%%%%%%%%%%%%%%%%%%%%%%%
    
    In the preferential attachment case, the proof is similar, except
    that we apply the fact that the attractiveness of any vertex $v < t$ at 
    time $t$ is at least $\frac{m}{2m(t-2)} = \frac{1}{2(t-2)}$.       
\end{proof}

\begin{lemma}[Conditional probability of two vertices with the same neighborhood]
    There exists a positive constant $C$ such that
    \[
        \Pr[N(n/2) > 0| N_A(n/2) > 0] > C
    \]
    for all $n$ sufficiently large.
\end{lemma}
\begin{proof}
    We condition on the lexicographically smallest pair $X$ from $S_{> n/2}$
    such that $A(X)$ holds.
    Let $D(u,v)$ be the event that the pair $(u,v)$ is the smallest pair from
    $S_{> n/2}$ for which $A$ holds.  Then
    \begin{align*}
        \Pr[N(n/2) > 0 | &N_A(n/2) > 0] \\
        &= \sum_{u < v \in S_{> n/2}} \Pr[N(n/2) > 0 | D(u,v)]\Pr[D(u,v) | N_A(n/2) > 0] \\
        &\geq \sum_{u < v \in S_{> n/2}} \Pr[B(u,v) | D(u,v)] \Pr[D(u,v) | N_A(n/2) > 0] \\
        &\geq C \sum_{u < v \in S_{> n/2}} \Pr[D(u,v) | N_A(n/2) > 0]
        = C.
    \end{align*}
    Here, the equalities are simply due to the law of total probability, and
    the first inequality is because the event
    $B(u,v)$ is a subset of the event $N(n/2) > 0$.
    The second inequality is by direct computation.  Note first that $D(u, v)$
    means that all lexicographically
    smaller pairs choose distinct pairs, and $u$ and $v$ choose the same pair
    (so that $v$ cannot choose $u$).  So
    \begin{align*}
        \Pr[B(u,v) | D(u,v)]
        &= \prod_{j = u+1}^v \Pr[j \text{ avoids } u | D(u,v)] \prod_{j=v+1}^n \Pr[j \text{ avoids } v, u | D(u,v)] \\
        &\geq \prod_{j= u+1}^{v-1} \left( \frac{(j-1)^2 - 2j - u}{(j-1)^2} \right) 
             \cdot 1 
             \cdot \prod_{j=v+1}^n \left( \frac{(j-1)^2 - 4j - u}{(j-1)^2} \right)\\
        &\geq \prod_{j=u+1}^{v-1} \left( 1 - \frac{c}{n} \right) 
             \prod_{j=v+1}^n \left( 1 - \frac{c}{n} \right)  \\
        &\geq \left( 1 - \frac{c}{n} \right)^n,
    \end{align*}
    where $c > 0$ is some constant.
    Here, the first inequality results from bounding the numerators below by
    giving upper bounds for the number of pairs
    that vertex $j$ must avoid in order to avoid $u$ and $v$ and for the number
    of pairs that $j$ must avoid in order to
    pick a pair that is distinct from the choices of all vertices $x$ such that
    $(x, j)$ is lexicographically smaller than
    $(u,v)$.
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    % Post-acceptance: Explain better how we're getting the first inequality.
    More specifically, conditioning on $D(u, v)$ reduces the number of pairs
    that $j$ may pick, but this number can be upper bounded by $(j-1)^2$, which
    gives the denominators.  Now, in order to avoid $u$, the $j$ in the first product
    must avoid at most $2j$ pairs (those candidate pairs with $u$ in the first
    and in the second coordinate, respectively).  We must also subtract $u$
    because of the condition that $j$ must avoid pairs which were picked by vertices
    $x$ such that $(x, j)$ is lexicographically smaller than $(u, v)$.  That is,
    $j$ must avoid at most $u$ other choices of pairs.  Similarly, for the second
    product, $j$ must avoid pairs containing either of $u$ or $v$ (at most $2j$
    possibilities for each of these), and $j$ avoids at most $u$ pairs which
    were chosen by vertices $x$ for which $(x, j)$ is lexicographically smaller
    than $(u, v)$.
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    The $1$ between the products is from the fact that $v$ avoids $u$
    with probability $1$, due to the conditioning
    by $D(u,v)$.
    The second inequality holds for all $n$ sufficiently large, since $j >
    \frac{n}{2}$.  The last inequality is because all
    factors are bounded above by $1$.
    Finally, by taking $n$ sufficiently large, the last value can be made
    arbitrarily close to $e^{-c}$.
    The proof in the preferential case is again similar, relying on the 
    previously stated lower bound on vertex attractiveness.
\end{proof}

%This completes the proof of Theorem \ref{M2Theorem}.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{Proof of Theorem~\ref{SlidingWindowTheorem}}
We will prove the claim when the expected window size is bounded by a constant
by examining the probability that vertices $n-1$ and $n$ both choose $m$ times vertex
$n-2$, thereby forming a cherry.  Let $\eta_i$ be the event that
vertex $i$ chooses $m$ times vertex $n-2$, so that the task is to find a lower
bound for $\Pr[\eta_{n-1} \lintersect \eta_n]$.

%By Markov's inequality, we have that, for any vertex $i$ and positive 
%$\epsilon$,
%\[
%    \Pr[W_i > W(1+\epsilon)]
%    \leq \frac{\E[W_i]}{W(1+\epsilon)}
%    \leq \frac{W}{W(1+\epsilon)} < 1.
%\]
%Thus, there is a constant $W'$ for which there exists some $\rho > 0$ 
%such that, for all $i$, $\Pr[W_i \leq W'] \geq \rho$.  In what follows,
%we set $W := W'$.
For any $i$, by the bound $\E[W_i] \leq w$, we have 
$\Pr[W_i \leq 2w]\geq 1/2$.
Now,
\begin{align*}
    \Pr[\eta_{n-1} \lintersect \eta_n] 
    &\stackrel{(a)}{\geq} \Pr[\eta_{n-1} \lintersect \eta_n | W_{n-1} \leq 2w, W_n \leq 2w]
      \cdot \Pr[W_{n-1} \leq 2w]\Pr[W_n \leq 2w] \\
    &\geq \tfrac14 \Pr[\eta_{n-1} \lintersect \eta_n | W_{n-1} \leq 2w, W_n \leq 2w] \\
    &= \tfrac14 \Pr[\eta_{n} | \eta_{n-1}, W_{n-1} \leq 2w, W_n \leq 2w] 
       \cdot \Pr[\eta_{n-1} | W_{n-1} \leq 2w, W_n \leq 2w]
\end{align*}
Here, (a) is by conditioning on the event that the window sizes of vertices 
$n-1$ and $n$ are bounded by $2w$, then applying the independence of the 
window sizes.

Now, in both the preferential and uniform attachment cases, 
%the condition that the window sizes are bounded by $2w$ implies that 
the total attractiveness
within the window $\{n-2w, \dots, n\}$ is bounded above by a constant.
Furthermore, since vertex $n-2$ makes $m$ choices, its attractiveness to
vertices $n-1$ and $n$ is at least a positive constant.  Thus, the two
remaining probabilities are both bounded below by a positive constant,
which completes the proof.

If the window sizes are uniformly bounded above by $w$ with probability $1$,
then symmetry with high probability results, roughly because the set $[n]$ may
be partitioned into subintervals, each having a positive probability of
contributing a symmetry; since window sizes are bounded, once a symmetry arises,
if a particular constant amount of time passes, the symmetry will never be
destroyed.
First, we choose an appropriate subinterval size $\zeta = 2w + 2$ and 
write $n=q\zeta + r$, for some integers $q, r$, with $0\leq r < \zeta$.
We then divide the interval $[r, \dots, n]$ into $q$ disjoint subintervals 
$I_1, \dots, I_q$, each of size $\zeta$.  Let $I_k$ be one of the intervals.
Consider the permutation $\pi_k = \pi$ that swaps the middlemost two vertices
(call them $x_k$ and $x_k + 1$) of $I_k$ and leaves all other vertices in $[n]$
fixed.  The event that $\pi_k \in \Aut(G_n)$ contains the event $\eta_j$ that
$x_j$ and $x_{j+1}$ both choose $x_{j-1}$ $m$ times and are unchosen.  We
will show that
\begin{align}
    \label{SlidingWindowBoundedWindowSizeEquation}
    \Pr\left[\neg \union_{j=1}^q \eta_j\right] \xrightarrow{n\to\infty} 0,
\end{align}
from which our claim follows.
We can write the probability in (\ref{SlidingWindowBoundedWindowSizeEquation})
as
\begin{align*}
    \Pr[ \neg \union_{j=1}^q \eta_j]
    = \prod_{j=1}^q \left(1 - \Pr\left[\eta_j | \intersect_{k=1}^{j-1} \neg \eta_k\right] \right).
\end{align*}
Now, the goal is to show that each factor in the product is bounded above
by a constant $C < 1$ (independent of $j$).  In both the preferential and
the uniform model, at any time $t$, the conditional probability of 
choosing any given 
vertex within the window of vertex $t$ 
is bounded above and below by positive constants (dependent only
on $m$ and $w$).  In particular, at time $t$, the attractiveness of any
vertex $v$ within the window of vertex $t$ is at least $m$ 
since $v$ makes $m$ choices, and the total attractiveness 
within the window is upper bounded by $w(w+1)m$ (since each vertex
within the window makes $m$ choices, and at most $w$ vertices may choose
a given vertex within the window at most 
$m$ times, and there are $w$ vertices in
the window).  Thus, the probability, for any choice of $t$, that $t$ chooses
any $v$ within its window is at least $\frac{m}{w(w+1)m} = \frac{1}{w(w+1)}$.
That is, every vertex within the window of $t$ has a non-negligible probability
of being chosen by $t$.  Note that this bound holds regardless of previous
vertex choices.

It follows from this that there is a constant $C < 1$ such that
\[
    1 - \Pr\left[\eta_j | \intersect_{k=1}^{j-1} \neg \eta_k\right] \leq C < 1,
\]
so that 
\[
    \Pr\left[\neg\union_{j=1}^q \eta_j\right] \leq C^q \xrightarrow{n\to\infty} 0,
\]
since $q \to \infty$ with $n$.  This completes the proof.


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%


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\end{thebibliography}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%



\end{document}
