isomorphism-impl.w

C++的一个好库。。。现在很流行

\documentclass[11pt]{report}

%\input{defs}
\usepackage{math}
\usepackage{jweb}
\usepackage{lgrind}
\usepackage{times}
\usepackage{fullpage}
\usepackage{graphicx}

\newif\ifpdf
\ifx\pdfoutput\undefined
   \pdffalse
\else
   \pdfoutput=1
   \pdftrue
\fi

\ifpdf
  \usepackage[
              pdftex,
              colorlinks=true, %change to true for the electronic version
              linkcolor=blue,filecolor=blue,pagecolor=blue,urlcolor=blue
              ]{hyperref}
\fi

\ifpdf
  \newcommand{\stlconcept}[1]{\href{http://www.sgi.com/tech/stl/#1.html}{{\small \textsf{#1}}}}
  \newcommand{\bglconcept}[1]{\href{http://www.boost.org/libs/graph/doc/#1.html}{{\small \textsf{#1}}}}
  \newcommand{\pmconcept}[1]{\href{http://www.boost.org/libs/property_map/#1.html}{{\small \textsf{#1}}}}
  \newcommand{\myhyperref}[2]{\hyperref[#1]{#2}}
  \newcommand{\vizfig}[2]{\begin{figure}[htbp]\centerline{\includegraphics*{#1.pdf}}\caption{#2}\label{fig:#1}\end{figure}}
\else
  \newcommand{\myhyperref}[2]{#2}
  \newcommand{\bglconcept}[1]{{\small \textsf{#1}}}
  \newcommand{\pmconcept}[1]{{\small \textsf{#1}}}
  \newcommand{\stlconcept}[1]{{\small \textsf{#1}}}
  \newcommand{\vizfig}[2]{\begin{figure}[htbp]\centerline{\includegraphics*{#1.eps}}\caption{#2}\label{fig:#1}\end{figure}}
\fi

\newcommand{\code}[1]{{\small{\em \textbf{#1}}}}


% jweb -np isomorphism-impl.w; dot -Tps out.dot -o out.eps; dot -Tps in.dot -o in.eps; latex isomorphism-impl.tex; dvips isomorphism-impl.dvi -o isomorphism-impl.ps

\setlength\overfullrule{5pt}
\tolerance=10000
\sloppy
\hfuzz=10pt

\makeindex

\newcommand{\isomorphic}{\cong}

\begin{document}

\title{An Implementation of Isomorphism Testing}
\author{Jeremy G. Siek}

\maketitle

\section{Introduction}

This paper documents the implementation of the \code{isomorphism()}
function of the Boost Graph Library.  The implementation was by Jeremy
Siek with algorithmic improvements and test code from Douglas Gregor.
The \code{isomorphism()} function answers the question, ``are these
two graphs equal?''  By \emph{equal}, we mean the two graphs have the
same structure---the vertices and edges are connected in the same
way. The mathematical name for this kind of equality is
\emph{isomorphic}.

An \emph{isomorphism} is a one-to-one mapping of the vertices in one
graph to the vertices of another graph such that adjacency is
preserved. Another words, given graphs $G_{1} = (V_{1},E_{1})$ and
$G_{2} = (V_{2},E_{2})$, an isomorphism is a function $f$ such that
for all pairs of vertices $a,b$ in $V_{1}$, edge $(a,b)$ is in $E_{1}$
if and only if edge $(f(a),f(b))$ is in $E_{2}$.

Both graphs must be the same size, so let $N = |V_1| = |V_2|$. The
graph $G_1$ is \emph{isomorphic} to $G_2$ if an isomorphism exists
between the two graphs, which we denote by $G_1 \isomorphic G_2$.

In the following discussion we will need to use several notions from
graph theory. The graph $G_s=(V_s,E_s)$ is a \emph{subgraph} of graph
$G=(V,E)$ if $V_s \subseteq V$ and $E_s \subseteq E$.  An
\emph{induced subgraph}, denoted by $G[V_s]$, of a graph $G=(V,E)$
consists of the vertices in $V_s$, which is a subset of $V$, and every
edge $(u,v)$ in $E$ such that both $u$ and $v$ are in $V_s$.  We use
the notation $E[V_s]$ to mean the edges in $G[V_s]$.

In some places we express a function as a set of pairs, so the set $f
= \{ \pair{a_1}{b_1}, \ldots, \pair{a_n}{b_n} \}$
means $f(a_i) = b_i$ for $i=1,\ldots,n$.

\section{Exhaustive Backtracking Search}

The algorithm used by the \code{isomorphism()} function is, at
first approximation, an exhaustive search implemented via
backtracking.  The backtracking algorithm is a recursive function. At
each stage we will try to extend the match that we have found so far.
So suppose that we have already determined that some subgraph of $G_1$
is isomorphic to a subgraph of $G_2$.  We then try to add a vertex to
each subgraph such that the new subgraphs are still isomorphic to one
another. At some point we may hit a dead end---there are no vertices
that can be added to extend the isomorphic subgraphs. We then
backtrack to previous smaller matching subgraphs, and try extending
with a different vertex choice. The process ends by either finding a
complete mapping between $G_1$ and $G_2$ and return true, or by
exhausting all possibilities and returning false.

We are going to consider the vertices of $G_1$ in a specific order
(more about this later), so assume that the vertices of $G_1$ are
labeled $1,\ldots,N$ according to the order that we plan to add them
to the subgraph.  Let $G_1[k]$ denote the subgraph of $G_1$ induced by
the first $k$ vertices, with $G_1[0]$ being an empty graph. At each
stage of the recursion we start with an isomorphism $f_{k-1}$ between
$G_1[k-1]$ and a subgraph of $G_2$, which we denote by $G_2[S]$, so
$G_1[k-1] \isomorphic G_2[S]$. The vertex set $S$ is the subset of
$V_2$ that corresponds via $f_{k-1}$ to the first $k-1$ vertices in
$G_1$. We try to extend the isomorphism by finding a vertex $v \in V_2
- S$ that matches with vertex $k$. If a matching vertex is found, we
have a new isomorphism $f_k$ with $G_1[k] \isomorphic G_2[S \union \{
v \}]$.

\begin{tabbing}
IS\=O\=M\=O\=RPH($k$, $S$, $f_{k-1}$) $\equiv$ \\
\>\textbf{if} ($k = |V_1|+1$) \\
\>\>\textbf{return} true \\
\>\textbf{for} each vertex $v \in V_2 - S$ \\
\>\>\textbf{if} (MATCH($k$, $v$)) \\
\>\>\>$f_k = f_{k-1} \union \pair{k}{v}$ \\
\>\>\>ISOMORPH($k+1$, $S \union \{ v \}$, $f_k$)\\
\>\>\textbf{else}\\
\>\>\>\textbf{return} false \\
\\
ISOMORPH($0$, $G_1$, $\emptyset$, $G_2$)
\end{tabbing}

The basic idea of the match operation is to check whether $G_1[k]$ is
isomorphic to $G_2[S \union \{ v \}]$. We already know that $G_1[k-1]
\isomorphic G_2[S]$ with the mapping $f_{k-1}$, so all we need to do
is verify that the edges in $E_1[k] - E_1[k-1]$ connect vertices that
correspond to the vertices connected by the edges in $E_2[S \union \{
v \}] - E_2[S]$. The edges in $E_1[k] - E_1[k-1]$ are all the
out-edges $(k,j)$ and in-edges $(j,k)$ of $k$ where $j$ is less than
or equal to $k$ according to the ordering.  The edges in $E_2[S \union
\{ v \}] - E_2[S]$ consists of all the out-edges $(v,u)$ and
in-edges $(u,v)$ of $v$ where $u \in S$.

\begin{tabbing}
M\=ATCH($k$, $v$) $\equiv$ \\
\>$out \leftarrow \forall (k,j) \in E_1[k] - E_1[k-1] \Big( (v,f(j)) \in E_2[S \union \{ v \}] - E_2[S] \Big)$ \\
\>$in \leftarrow \forall (j,k) \in E_1[k] - E_1[k-1] \Big( (f(j),v) \in E_2[S \union \{ v \}] - E_2[S] \Big)$ \\
\>\textbf{return} $out \Land in$ 
\end{tabbing}

The problem with the exhaustive backtracking algorithm is that there
are $N!$ possible vertex mappings, and $N!$ gets very large as $N$
increases, so we need to prune the search space. We use the pruning
techniques described in
\cite{deo77:_new_algo_digraph_isomorph,fortin96:_isomorph,reingold77:_combin_algo}
that originated in
\cite{sussenguth65:_isomorphism,unger64:_isomorphism}.

\section{Vertex Invariants}
\label{sec:vertex-invariants}

One way to reduce the search space is through the use of \emph{vertex
invariants}. The idea is to compute a number for each vertex $i(v)$
such that $i(v) = i(v')$ if there exists some isomorphism $f$ where
$f(v) = v'$. Then when we look for a match to some vertex $v$, we only
need to consider those vertices that have the same vertex invariant
number. The number of vertices in a graph with the same vertex
invariant number $i$ is called the \emph{invariant multiplicity} for
$i$.  In this implementation, by default we use the out-degree of the
vertex as the vertex invariant, though the user can also supply there
own invariant function. The ability of the invariant function to prune
the search space varies widely with the type of graph.

As a first check to rule out graphs that have no possibility of
matching, one can create a list of computed vertex invariant numbers
for the vertices in each graph, sort the two lists, and then compare
them.  If the two lists are different then the two graphs are not
isomorphic.  If the two lists are the same then the two graphs may be
isomorphic.

Also, we extend the MATCH operation to use the vertex invariants to
help rule out vertices.

\begin{tabbing}
M\=A\=T\=C\=H-INVAR($k$, $v$) $\equiv$ \\
\>$out \leftarrow \forall (k,j) \in E_1[k] - E_1[k-1] \Big( (v,f(j)) \in E_2[S \union \{ v \}] - E_2[S] \Land i(v) = i(k) \Big)$ \\
\>$in \leftarrow \forall (j,k) \in E_1[k] - E_1[k-1] \Big( (f(j),v) \in E_2[S \union \{ v \}] - E_2[S] \Land i(v) = i(k) \Big)$ \\
\>\textbf{return} $out \Land in$ 
\end{tabbing}

\section{Vertex Order}

A good choice of the labeling for the vertices (which determines the
order in which the subgraph $G_1[k]$ is grown) can also reduce the
search space. In the following we discuss two labeling heuristics.

\subsection{Most Constrained First}

Consider the most constrained vertices first.  That is, examine
lower-degree vertices before higher-degree vertices. This reduces the
search space because it chops off a trunk before the trunk has a
chance to blossom out. We can generalize this to use vertex
invariants. We examine vertices with low invariant multiplicity
before examining vertices with high invariant multiplicity.

\subsection{Adjacent First}

The MATCH operation only considers edges when the other vertex already
has a mapping defined. This means that the MATCH operation can only
weed out vertices that are adjacent to vertices that have already been
matched. Therefore, when choosing the next vertex to examine, it is
desirable to choose one that is adjacent a vertex already in $S_1$.

\subsection{DFS Order, Starting with Lowest Multiplicity}

For this implementation, we combine the above two heuristics in the
following way. To implement the ``adjacent first'' heuristic we apply
DFS to the graph, and use the DFS discovery order as our vertex
order. To comply with the ``most constrained first'' heuristic we
order the roots of our DFS trees by invariant multiplicity.


\section{Implementation}

The following is the public interface for the \code{isomorphism}
function. The input to the function is the two graphs $G_1$ and $G_2$,
mappings from the vertices in the graphs to integers (in the range
$[0,|V|)$), and a vertex invariant function object. The output of the
function is an isomorphism $f$ if there is one. The \code{isomorphism}
function returns true if the graphs are isomorphic and false
otherwise. The requirements on type template parameters are described
below in the section ``Concept checking''.

@d Isomorphism Function Interface
@{
template <typename Graph1, typename Graph2, 
          typename IndexMapping, 
          typename VertexInvariant1, typename VertexInvariant2,
          typename IndexMap1, typename IndexMap2>
bool isomorphism(const Graph1& g1, const Graph2& g2, 
                 IndexMapping f, 
                 VertexInvariant1 invariant1, VertexInvariant2 invariant2,
                 IndexMap1 index_map1, IndexMap2 index_map2)
@}

The main outline of the \code{isomorphism} function is as
follows. Most of the steps in this function are for setting up the
vertex ordering, first ordering the vertices by invariant multiplicity
and then by DFS order. The last step is the call to the
\code{isomorph} function which starts the backtracking search.

@d Isomorphism Function Body
@{
{
  @<Some type definitions and iterator declarations@>
  @<Concept checking@>
  @<Quick return with false if $|V_1| \neq |V_2|$@>
  @<Compute vertex invariants@>
  @<Quick return if the graph's invariants do not match@>
  @<Compute invariant multiplicity@>
  @<Sort vertices by invariant multiplicity@>
  @<Order the vertices by DFS discover time@>
  @<Order the edges by DFS discover time@>
  @<Invoke recursive \code{isomorph} function@>
}
@}

There are some types that will be used throughout the function, which
we create shortened names for here. We will also need vertex
iterators for \code{g1} and \code{g2} in several places, so we define
them here.

@d Some type definitions and iterator declarations
@{
typedef typename graph_traits<Graph1>::vertex_descriptor vertex1_t;
typedef typename graph_traits<Graph2>::vertex_descriptor vertex2_t;
typedef typename graph_traits<Graph1>::vertices_size_type size_type;
typename graph_traits<Graph1>::vertex_iterator i1, i1_end;
typename graph_traits<Graph2>::vertex_iterator i2, i2_end;
@}

We use the Boost Concept Checking Library to make sure that the type
arguments to the function fulfill there requirements. The
\code{Graph1} type must be a \bglconcept{VertexListGraph} and a
\bglconcept{EdgeListGraph}. The \code{Graph2} type must be a
\bglconcept{VertexListGraph} and a
\bglconcept{BidirectionalGraph}. The \code{IndexMapping} type that
represents the isomorphism $f$ must be a
\pmconcept{ReadWritePropertyMap} that maps from vertices in $G_1$ to
vertices in $G_2$. The two other index maps are
\pmconcept{ReadablePropertyMap}s from vertices in $G_1$ and $G_2$ to
unsigned integers.

@d Concept checking
@{