AimRT/_deps/boost-src/libs/graph/example/actor_clustering.cpp

// Copyright 2004 The Trustees of Indiana University.

// Use, modification and distribution is subject to 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)

//  Authors: Douglas Gregor
//           Andrew Lumsdaine

// This program performs betweenness centrality (BC) clustering on the
// actor collaboration graph available at
// http://www.nd.edu/~networks/resources/actor/actor.dat.gz and outputs the
// result of clustering in Pajek format.
//
// This program mimics the BC clustering algorithm program implemented
// by Shashikant Penumarthy for JUNG, so that we may compare results
// and timings.
#include <boost/graph/bc_clustering.hpp>
#include <boost/graph/adjacency_list.hpp>
#include <boost/graph/graph_traits.hpp>
#include <fstream>
#include <iostream>
#include <string>
#include <boost/tokenizer.hpp>
#include <boost/lexical_cast.hpp>
#include <map>

using namespace boost;

struct Actor
{
    Actor(int id = -1) : id(id) {}

    int id;
};

typedef adjacency_list< vecS, vecS, undirectedS, Actor,
    property< edge_centrality_t, double > >
    ActorGraph;
typedef graph_traits< ActorGraph >::vertex_descriptor Vertex;
typedef graph_traits< ActorGraph >::edge_descriptor Edge;

void load_actor_graph(std::istream& in, ActorGraph& g)
{
    std::map< int, Vertex > actors;

    std::string line;
    while (getline(in, line))
    {
        std::vector< Vertex > actors_in_movie;

        // Map from the actor numbers on this line to the actor vertices
        typedef tokenizer< char_separator< char > > Tok;
        Tok tok(line, char_separator< char >(" "));
        for (Tok::iterator id = tok.begin(); id != tok.end(); ++id)
        {
            int actor_id = lexical_cast< int >(*id);
            std::map< int, Vertex >::iterator v = actors.find(actor_id);
            if (v == actors.end())
            {
                Vertex new_vertex = add_vertex(Actor(actor_id), g);
                actors[actor_id] = new_vertex;
                actors_in_movie.push_back(new_vertex);
            }
            else
            {
                actors_in_movie.push_back(v->second);
            }
        }

        for (std::vector< Vertex >::iterator i = actors_in_movie.begin();
             i != actors_in_movie.end(); ++i)
        {
            for (std::vector< Vertex >::iterator j = i + 1;
                 j != actors_in_movie.end(); ++j)
            {
                if (!edge(*i, *j, g).second)
                    add_edge(*i, *j, g);
            }
        }
    }
}

template < typename Graph, typename VertexIndexMap, typename VertexNameMap >
std::ostream& write_pajek_graph(std::ostream& out, const Graph& g,
    VertexIndexMap vertex_index, VertexNameMap vertex_name)
{
    out << "*Vertices " << num_vertices(g) << '\n';
    typedef typename graph_traits< Graph >::vertex_iterator vertex_iterator;
    for (vertex_iterator v = vertices(g).first; v != vertices(g).second; ++v)
    {
        out << get(vertex_index, *v) + 1 << " \"" << get(vertex_name, *v)
            << "\"\n";
    }

    out << "*Edges\n";
    typedef typename graph_traits< Graph >::edge_iterator edge_iterator;
    for (edge_iterator e = edges(g).first; e != edges(g).second; ++e)
    {
        out << get(vertex_index, source(*e, g)) + 1 << ' '
            << get(vertex_index, target(*e, g)) + 1 << " 1.0\n"; // HACK!
    }
    return out;
}

class actor_clustering_threshold : public bc_clustering_threshold< double >
{
    typedef bc_clustering_threshold< double > inherited;

public:
    actor_clustering_threshold(
        double threshold, const ActorGraph& g, bool normalize)
    : inherited(threshold, g, normalize), iter(1)
    {
    }

    bool operator()(double max_centrality, Edge e, const ActorGraph& g)
    {
        std::cout << "Iter: " << iter
                  << " Max Centrality: " << (max_centrality / dividend)
                  << std::endl;
        ++iter;
        return inherited::operator()(max_centrality, e, g);
    }

private:
    unsigned int iter;
};

int main(int argc, char* argv[])
{
    std::string in_file;
    std::string out_file;
    double threshold = -1.0;
    bool normalize = false;

    // Parse command-line options
    {
        int on_arg = 1;
        while (on_arg < argc)
        {
            std::string arg(argv[on_arg]);
            if (arg == "-in")
            {
                ++on_arg;
                assert(on_arg < argc);
                in_file = argv[on_arg];
            }
            else if (arg == "-out")
            {
                ++on_arg;
                assert(on_arg < argc);
                out_file = argv[on_arg];
            }
            else if (arg == "-threshold")
            {
                ++on_arg;
                assert(on_arg < argc);
                threshold = lexical_cast< double >(argv[on_arg]);
            }
            else if (arg == "-normalize")
            {
                normalize = true;
            }
            else
            {
                std::cerr << "Unrecognized parameter \"" << arg << "\".\n";
                return -1;
            }
            ++on_arg;
        }

        if (in_file.empty() || out_file.empty() || threshold < 0)
        {
            std::cerr << "error: syntax is actor_clustering [options]\n\n"
                      << "options are:\n"
                      << "\t-in <infile>\tInput file\n"
                      << "\t-out <outfile>\tOutput file\n"
                      << "\t-threshold <value>\tA threshold value\n"
                      << "\t-normalize\tNormalize edge centrality scores\n";
            return -1;
        }
    }

    ActorGraph g;

    // Load the actor graph
    {
        std::cout << "Building graph." << std::endl;
        std::ifstream in(in_file.c_str());
        if (!in)
        {
            std::cerr << "Unable to open file \"" << in_file
                      << "\" for input.\n";
            return -2;
        }
        load_actor_graph(in, g);
    }

    // Run the algorithm
    std::cout << "Clusting..." << std::endl;
    betweenness_centrality_clustering(g,
        actor_clustering_threshold(threshold, g, normalize),
        get(edge_centrality, g));

    // Output the graph
    {
        std::cout << "Writing graph to file: " << out_file << std::endl;
        std::ofstream out(out_file.c_str());
        if (!out)
        {
            std::cerr << "Unable to open file \"" << out_file
                      << "\" for output.\n";
            return -3;
        }
        write_pajek_graph(out, g, get(vertex_index, g), get(&Actor::id, g));
    }
    return 0;
}