
BoostCommit : 
From: asutton_at_[hidden]
Date: 20070822 11:00:39
Author: asutton
Date: 20070822 11:00:36 EDT (Wed, 22 Aug 2007)
New Revision: 38845
URL: http://svn.boost.org/trac/boost/changeset/38845
Log:
Added clustering coef docs
Fixed typo in b&k
Added:
sandbox/SOC/2007/graphs/libs/graph/doc/reference/clustering_coefficient.qbk (contents, props changed)
Text files modified:
sandbox/SOC/2007/graphs/libs/graph/doc/reference/bron_kerbosch_all_cliques.qbk  6 +++
sandbox/SOC/2007/graphs/libs/graph/doc/reference/reference.qbk  1 +
2 files changed, 4 insertions(+), 3 deletions()
Modified: sandbox/SOC/2007/graphs/libs/graph/doc/reference/bron_kerbosch_all_cliques.qbk
==============================================================================
 sandbox/SOC/2007/graphs/libs/graph/doc/reference/bron_kerbosch_all_cliques.qbk (original)
+++ sandbox/SOC/2007/graphs/libs/graph/doc/reference/bron_kerbosch_all_cliques.qbk 20070822 11:00:36 EDT (Wed, 22 Aug 2007)
@@ 71,10 +71,10 @@
*Requirements:* The `Graph` type must be a model of the [AdjacencyMatrix],
[IncidenceGraph] concept and the [VertexIndexGraph]
 concepts[footnote Any `Graph` typen that implements the `edge()`
+ concepts[footnote Any `Graph` type that implements the `edge()`
function will satisfy the expression requirements for the
[AdjacencyMatrix], but may incur additional overhead due nonconstant
 implementations.].
+ time complexity.].
]
]
[
@@ 136,7 +136,7 @@
concepts[footnote Any `Graph` typen that implements the `edge()`
function will satisfy the expression requirements for the
[AdjacencyMatrix], but may incur additional overhead due nonconstant
 implementations.].
+ time complexity.].
]
]
]
Added: sandbox/SOC/2007/graphs/libs/graph/doc/reference/clustering_coefficient.qbk
==============================================================================
 (empty file)
+++ sandbox/SOC/2007/graphs/libs/graph/doc/reference/clustering_coefficient.qbk 20070822 11:00:36 EDT (Wed, 22 Aug 2007)
@@ 0,0 +1,199 @@
+[/
+ / Copyright (c) 2007 Andrew Sutton
+ /
+ / Distributed under the Boost Software License, Version 1.0. (See accompanying
+ / file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
+ /]
+
+[section Clustering Coefficient]
+[template ex_clustering_coefficient[] [link
+ boost_graph.reference.algorithms.measures.closeness_centrality.examples.closeness_centrality
+ Closeness Centrality Example]]
+
+[heading Overview]
+The /clustering coefficient/ is a measure used in network analysis to describe a
+connective property of vertices. The clustering coefficient of a vertex gives the
+probability that two neighbors of that vertex are, themselves, connected. In social
+networks, this is often interpreted as the probability that a persons friends are
+also friends.
+
+This measure is derived from two different properties of a vertex /v/. The first is the
+number of possible paths through that vertex. The number of paths through a vertex
+can be counted as the number of possible ways to connect the neighbors (adjacent
+vertices) of /v/. This number differs for directed and undirected graphs. For directed
+graphs it is given as:
+
+[$images/eq/num_paths_directed.png]
+
+Where /d(v)/ is the outdegree of the vertex /v/. For undirected graphs:
+
+[$images/eq/num_paths_undirected.png]
+
+Note that for undirected graphs the outdegree of /v/ is the same as the degree
+of /v/.
+
+The second property is the number of triangles centered on the vertex /v/. Triangles
+are counted as each pair of neighbors (adjacent vertices) /p/ and /q/ that are
+connected (i.e., and edge exists between /p/ and /q/). Note that if /p/ and /q/
+are connected, they form a distinct triangle /{v, p, q}/. For directed graphs, the
+edges /(p, q)/ and /(q, p)/ can form two distinct triangles. The number of triangles
+centered on a vertex is given as:
+
+[$images/eq/num_triangles.png]
+
+Where ['e[sub pq]] is an edge connecting vertices /p/ and /q/ in the edge set of
+the graph. The clustering coefficient of the vertex is then computed as:
+
+[$images/eq/clustering_coef.png]
+
+Note that the clustering coefficient of a vertex /v/ is 0 if none of its neighbors
+are connected to each other. Its clustering coefficient is 1 if all of its neighbors
+are connected to each other.
+
+The /mean clustering coefficient/ can be computed for the entire graph as quantification
+of the /smallworld property/. A graph is a small world property if its clustering
+coefficient is significantly higher than a random graph over the same vertex set.
+
+Consider the following social network represented by an undirected graph in
+Figure 1.
+
+[figure
+ images/reference/social_network.png
+ *Figure 1.* A network of friends.
+]
+
+Computing the clustering coefficient for each person in this network shows that
+Frank has a clustering coefficient of 0.1. This implies that while Frank has many
+friends, his friends are not necessarily friendly with each other. On the other
+hand Jill, Anne, and Howard each have a value of 1.0, meaning that they enjoy
+participation in a social clique.
+
+The mean clustering coefficient of this graph is 0.4. Unfortunately, since the
+graph is so small, one cannot determine whether or not this network does exhibit
+the smallworld property.
+
+[section [^clustering_coefficient()]]
+ #include <boost/graph/clustering_coefficient.hpp>
+
+ template <typename Graph, typename Vertex>
+ float clustering_coefficient(const Graph& g, Vertex v)
+
+ template <typename ResultType, typename Graph, typename Vertex>
+ ResultType clustering_coefficient(const Graph& g, Vertex v)
+
+The `clustering_coefficient()` function returns the clustering coefficient of
+the given vertex. The second variant allows the caller to explicitly specify
+the type of the clustering coefficient.
+
+[heading Parameters]
+[table
+ [[Type] [Parameter] [Description]]
+ [
+ [template] [`ResultType`]
+ [
+ The `ResultType` template parmeter explitly specifies the the
+ return type of the function. If not given, the return type is `float`.
+
+ *Requirements:* The return type is required to model the [NumericValue]
+ concept.
+ ]
+ ]
+ [
+ [required, in] [`const Graph& g`]
+ [
+ The graph for which vertex measures are being comptued.
+
+ *Requirements:* The `Graph` type must be a model of the [IncidenceGraph],
+ [AdjacencyGraph] and [AdjacencyMatrix] concepts[footnote Any `Graph` type
+ that implements the `edge()` function will satisfy the expression requirements
+ for the [AdjacencyMatrix], but may incur additional overhead due nonconstant
+ time complexity.].
+ ]
+ ]
+]
+
+[heading Return]
+The `clustering_coefficient()` function returns the clustering coefficient of the
+vertex. The return type is either `float` or the type specified by the user.
+
+[heading Complexity]
+The `clustering_coefficient()` function returns in ['O(d(v)[super 2]] where
+/d(v)/ is the degree of /v/.
+[endsect]
+
+[section [^all_clustering_cofficients()]]
+ #include <boost/graph/clustering_coefficient.hpp>
+
+ template <typename Graph, typename ClusteringMap>
+ typename property_traits<ClusteringMap>::value_type
+ all_clustering_coefficients(const Graph& g, ClusteringMap cm)
+
+Compute the clustering coefficients for each vertex and return the mean clustering
+coefficient to the caller.
+
+[heading Parameters]
+[table
+ [[Type] [Parameter] [Description]]
+ [
+ [required, in] [`const Graph& g`]
+ [
+ The graph for which vertex measures are being comptued.
+
+ *Requirements:* The `Graph` type must be a model of the [VertexListGraph],
+ [IncidenceGraph], [AdjacencyGraph] and [AdjacencyMatrix] concepts[footnote Any
+ `Graph` type that implements the `edge()` function will satisfy the expression
+ requirements for the [AdjacencyMatrix], but may incur additional overhead due
+ nonconstant time complexity.].
+ ]
+ ]
+ [
+ [required, in] [`ClusteringMap cm`]
+ [
+ The clustering map `cm` stores the clustering coefficient of each
+ vertex in the graph `g`.
+
+ *Requirements:* The `ClusteringMap` type must modelt the [WritablePropertyMap]
+ concept.
+ ]
+ ]
+]
+
+[heading Complexity]
+The `all_clustering_coefficients()` function returns in ['O(nd[super 2])] where
+/d/ is the mean (average) degree of vertices in the graph.
+[endsect]
+
+[section [^num_paths_through_vertex()]]
+[endsect]
+
+[section [^num_triangles_on_vertex()]]
+[endsect]
+
+[section Examples]
+[heading Clustering Coefficient]
+This example computes both the clustering coefficient for each vertex in a graph and
+the mean clustering coefficient for the graph, printing them to standard output.
+
+[code_clustering_coefficient]
+
+If this program is given the `social_network.graph` file as input which represents
+the graph shown in the
+[link boost_graph.reference.algorithms.measures.clustering_coefficient.overview Overview],
+the output will be:
+
+[pre
+Scott 0.166667
+Jill 1
+Mary 0.333333
+Bill 0
+Josh 0
+Frank 0.1
+Laurie 0
+Anne 1
+Howard 1
+mean clustering coefficient: 0.4
+]
+
+[endsect]
+
+[endsect]
Modified: sandbox/SOC/2007/graphs/libs/graph/doc/reference/reference.qbk
==============================================================================
 sandbox/SOC/2007/graphs/libs/graph/doc/reference/reference.qbk (original)
+++ sandbox/SOC/2007/graphs/libs/graph/doc/reference/reference.qbk 20070822 11:00:36 EDT (Wed, 22 Aug 2007)
@@ 71,6 +71,7 @@
[include betweenness_centrality.qbk]
[include mean_geodesic.qbk]
[include eccentricity.qbk]
+[include clustering_coefficient.qbk]
[endsect] [/Measures]
[endsect] [/Algorithms]
BoostCommit list run by bdawes at acm.org, david.abrahams at rcn.com, gregod at cs.rpi.edu, cpdaniel at pacbell.net, john at johnmaddock.co.uk