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graph-tool
Commit f12d5416 authored by Tiago Peixoto's avatar Tiago Peixoto
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Modify absolute_centrality implementation 

This dumps the random-path approach, and implements a deterministic,
priority queue algorithm.
parent c6c01d62
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Showing with 367 additions and 194 deletions
src/graph/centrality/graph_absolute_trust.cc
View file @ f12d5416
......@@ -22,9 +22,6 @@
#include "graph.hh"
#include "graph_selectors.hh"
#include <tr1/random>
typedef std::tr1::mt19937 rng_t;
#include "graph_absolute_trust.hh"
using namespace std;
......@@ -32,10 +29,8 @@ using namespace boost;
using namespace graph_tool;
void absolute_trust(GraphInterface& g, int64_t source, boost::any c,
boost::any t, size_t iter, bool reversed, size_t seed)
boost::any t, size_t n_paths, double epsilon, bool reversed)
{
rng_t rng(static_cast<rng_t::result_type>(seed));
if (!belongs<edge_floating_properties>()(c))
throw ValueException("edge property must be of floating point value type");
if (!belongs<vertex_floating_vector_properties>()(t))
......@@ -43,8 +38,7 @@ void absolute_trust(GraphInterface& g, int64_t source, boost::any c,
run_action<>()(g,
bind<void>(get_absolute_trust(), _1, g.GetVertexIndex(),
source, _2, _3, iter, reversed,
ref(rng)),
source, _2, _3, n_paths, epsilon, reversed),
edge_floating_properties(),
vertex_floating_vector_properties())(c, t);
}
......
src/graph/centrality/graph_absolute_trust.hh
View file @ f12d5416
......@@ -23,8 +23,10 @@
#include "graph_util.hh"
#include <tr1/unordered_set>
#include <tr1/random>
#include <boost/functional/hash.hpp>
#include <tr1/tuple>
#include <algorithm>
#include "minmax.hh"
#include <iostream>
......@@ -32,183 +34,151 @@ namespace graph_tool
{
using namespace std;
using namespace boost;
using std::tr1::get;
using std::tr1::tuple;
template <class Path>
struct path_cmp
{
path_cmp(vector<Path>& paths): _paths(paths) {}
vector<Path>& _paths;
typedef size_t first_argument_type;
typedef size_t second_argument_type;
typedef bool result_type;
inline bool operator()(size_t a, size_t b)
{
if (get<0>(_paths[a]).first == get<0>(_paths[b]).first)
return get<1>(_paths[a]).size() > get<1>(_paths[b]).size();
return get<0>(_paths[a]).first < get<0>(_paths[b]).first;
}
};
struct get_absolute_trust
{
template <class Graph, class VertexIndex, class TrustMap,
class InferredTrustMap>
void operator()(Graph& g, VertexIndex vertex_index, int64_t source,
TrustMap c, InferredTrustMap t, size_t n_iter,
bool reversed, rng_t& rng) const
TrustMap c, InferredTrustMap t, size_t n_paths,
double epsilon, bool reversed) const
{
typedef typename graph_traits<Graph>::vertex_descriptor vertex_t;
typedef typename property_traits<TrustMap>::value_type c_type;
typedef typename property_traits<InferredTrustMap>::value_type
::value_type t_type;
unchecked_vector_property_map<vector<t_type>, VertexIndex>
t_count(vertex_index, num_vertices(g));
unchecked_vector_property_map<vector<size_t>, VertexIndex>
v_mark(vertex_index, num_vertices(g));
typedef tuple<pair<t_type, t_type>, tr1::unordered_set<vertex_t>,
size_t > path_t;
// init inferred trust t
double delta = epsilon+1;
int i, N = num_vertices(g);
#pragma omp parallel for default(shared) private(i) schedule(dynamic)
for (i = (source == -1) ? 0 : source;
i < ((source == -1) ? N : source + 1); ++i)
{
typename graph_traits<Graph>::vertex_descriptor v =
vertex(i, g);
vertex_t v = vertex(i, g);
if (v == graph_traits<Graph>::null_vertex())
continue;
t[v].resize(N);
t_count[v].resize(N,0);
v_mark[v].resize(N,0);
}
#pragma omp parallel for default(shared) private(i) schedule(dynamic)
for (i = (source == -1) ? 0 : source;
i < ((source == -1) ? N : source + 1); ++i)
{
typename graph_traits<Graph>::vertex_descriptor v =
vertex(i, g);
if (v == graph_traits<Graph>::null_vertex())
continue;
// path priority queue
vector<path_t> paths(1);
vector<size_t> free_indexes;
typedef double_priority_queue<size_t, path_cmp<path_t> > queue_t;
queue_t queue = queue_t(path_cmp<path_t>(paths));
get<0>(paths.back()).first = get<0>(paths.back()).second = 1;
get<1>(paths.back()).insert(v);
get<2>(paths.back()) = vertex_index[v];
queue.push(0);
// walk hash set
tr1::unordered_set<size_t> path_set;
tr1::uniform_int<size_t>
random_salt(0, numeric_limits<size_t>::max());
size_t salt;
t[v].resize(num_vertices(g));
unchecked_vector_property_map<t_type, VertexIndex>
sum_weight(vertex_index, num_vertices(g));
size_t count = 1;
while (!queue.empty())
{
#pragma omp critical
salt = random_salt(rng);
}
size_t pi = queue.top();
queue.pop_top();
vertex_t w = vertex(get<2>(paths[pi]), g);
for (size_t bias = 0; bias < 3; ++bias)
for (size_t iter = 0; iter < n_iter; ++iter)
typename graph_traits<Graph>::out_edge_iterator e, e_end;
for (tie(e, e_end) = out_edges(w, g); e != e_end; ++e)
{
typename graph_traits<Graph>::vertex_descriptor pos = v;
t_type pos_t = 1.0;
t_type pweight = 1.0;
v_mark[v][vertex_index[v]] = bias*n_iter + iter + 1;
size_t path_hash = salt;
hash_combine(path_hash, i);
// start a self-avoiding walk from vertex v
vector<typename graph_traits<Graph>::edge_descriptor>
out_es;
vector<t_type> out_prob;
do
vertex_t a = target(*e, g);
// no loops
if (get<1>(paths[pi]).find(a) == get<1>(paths[pi]).end())
{
out_es.clear();
out_prob.clear();
// obtain list of candidate edges to follow
typename graph_traits<Graph>::out_edge_iterator e,
e_end;
for (tie(e, e_end) = out_edges(pos, g); e != e_end; ++e)
// new path; only follow non-zero paths
t_type old = (sum_weight[a] > 0) ?
t[v][a]/sum_weight[a] : 0;
if (c[*e] > 0 || (reversed &&
get<1>(paths[pi]).size() == 1))
{
typename graph_traits<Graph>::vertex_descriptor t =
target(*e,g);
if (v_mark[v][vertex_index[t]] <
bias*n_iter + iter + 1)
size_t npi;
if (free_indexes.empty())
{
if (c[*e] < 0 || c[*e] > 1)
continue;
out_es.push_back(*e);
if (bias != 1)
{
double prob = c[*e];
if (bias == 2)
prob = 1.0 - prob;
if (out_prob.empty())
out_prob.push_back(prob);
else
out_prob.push_back(out_prob.back() +
prob);
}
paths.push_back(paths[pi]); // clone last path
npi = paths.size()-1;
}
else
{
npi = free_indexes.back();
free_indexes.pop_back();
paths[npi] = paths[pi];
}
}
if (!out_es.empty())
{
// select edge according to its probability
typename graph_traits<Graph>::edge_descriptor e;
if (!out_prob.empty() && out_prob.back() > 0)
path_t& np = paths[npi];
if (!reversed)
{
typedef tr1::uniform_real<t_type> dist_t;
tr1::variate_generator<rng_t&, dist_t>
random(rng, dist_t(0, out_prob.back()));
t_type u;
{
#pragma omp critical
u = random();
}
e = out_es[upper_bound(out_prob.begin(),
out_prob.end(), u) -
out_prob.begin()];
get<0>(np).second = get<0>(np).first;
get<0>(np).first *= c[*e];
}
else
{
tr1::uniform_int<size_t>
random(0, out_es.size()-1);
#pragma omp critical
e = out_es[random(rng)];
if (get<1>(np).size() > 1)
get<0>(np).second *= c[*e];
get<0>(np).first *= c[*e];
}
get<1>(np).insert(a);
get<2>(np) = vertex_index[a];
// new position in random walk
pos = target(e,g);
size_t posi = vertex_index[pos];
// update current path trust
pos_t *= c[e];
if (reversed && boost::source(e, g) != v)
pweight *= c[e];
// get path hash
hash_combine(path_hash, posi);
if (path_set.find(path_hash) == path_set.end())
t[v][a] += get<0>(np).first*get<0>(np).second;
sum_weight[a] += get<0>(np).second;
queue.push(npi);
if (n_paths > 0 && queue.size() > n_paths)
{
// if new path, modify vertex trust score
path_set.insert(path_hash);
t_type old = 0;
if (t_count[v][posi] > 0)
old = t[v][posi]/t_count[v][posi];
t[v][posi] += pos_t*pweight;
t_count[v][posi] += pweight;
size_t bi = queue.bottom();
queue.pop_bottom();
free_indexes.push_back(bi);
}
if (!reversed)
pweight *= c[e];
// stop if weight drops to zero
if (pweight == 0)
break;
// mark vertex
v_mark[v][posi] = bias*n_iter + iter + 1;
}
else
{
sum_weight[a] += get<0>(paths[pi]).first;
}
if (sum_weight[a] > 0)
delta += abs(old-t[v][a]/sum_weight[a]);
}
while (!out_es.empty());
}
}
free_indexes.push_back(pi);
#pragma omp parallel for default(shared) private(i) schedule(dynamic)
for (i = (source == -1) ? 0 : source;
i < ((source == -1) ? N : source + 1); ++i)
{
typename graph_traits<Graph>::vertex_descriptor v = vertex(i, g);
if (v == graph_traits<Graph>::null_vertex())
continue;
for (size_t j = 0; j < size_t(N); ++j)
if (t_count[v][j] > 0)
t[v][j] /= t_count[v][j];
if ((count % N) == 0)
{
if (delta < epsilon)
break;
else
delta = 0;
}
count++;
}
typename graph_traits<Graph>::vertex_iterator w, w_end;
for (tie(w, w_end) = vertices(g); w != w_end; ++w)
{
if (sum_weight[*w] > 0)
t[v][*w] /= sum_weight[*w];
}
}
}
};
......
src/graph/centrality/minmax.hh 0 → 100644
View file @ f12d5416
// graph-tool -- a general graph modification and manipulation thingy
//
// Copyright (C) 2007 Tiago de Paula Peixoto <tiago@forked.de>
//
// This program is free software; you can redistribute it and/or
// modify it under the terms of the GNU General Public License
// as published by the Free Software Foundation; either version 3
// of the License, or (at your option) any later version.
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program. If not, see <http://www.gnu.org/licenses/>.
#ifndef MINMAX_HH
#define MINMAX_HH
#include <vector>
#include <boost/mpl/bool.hpp>
#include <iostream>
using namespace std;
template <class Value, class Compare = std::less<Value> >
class double_priority_queue
{
public:
double_priority_queue() {}
double_priority_queue(const Compare& cmp): _cmp(cmp) {}
size_t size() { return _queue.size(); }
bool empty() { return _queue.empty(); }
void push(const Value& v)
{
_queue.push_back(v);
bubble_up(_queue.size()-1);
}
void pop_top()
{
// for (size_t i = 0; i < _queue.size(); ++i)
// if (_cmp(top(), _queue[i]))
// {
// cout << size_t(log2(i+1)) << endl;
// cout << "ops top" << endl;
// abort();
// }
size_t i = &top() - &_queue[0];
swap(_queue[i], _queue.back());
_queue.pop_back();
if (!_queue.empty())
trickle_down(i);
}
void pop_bottom()
{
return;
swap(_queue.front(), _queue.back());
_queue.pop_back();
if (!_queue.empty())
trickle_down(0);
}
const Value& top()
{
if (_queue.size() < 2)
return _queue[0];
if (_queue.size() < 3)
return _queue[1];
if (_cmp(_queue[1], _queue[2]))
return _queue[2];
else
return _queue[1];
}
const Value& bottom()
{
// for (size_t i = 0; i < _queue.size(); ++i)
// if (_cmp(_queue[i], _queue.front()))
// cout << "ops bottom" << endl;
return _queue.front();
}
// general comparison: if IsMin is mpl::true_, then a < b, otherwise a > b
// is computed
template <class IsMin>
bool cmp(size_t a, size_t b, IsMin)
{
if (IsMin::value)
return _cmp(_queue[a], _queue[b]);
else
return _cmp(_queue[b], _queue[a]);
}
void trickle_down(size_t i)
{
if ((size_t(log2(i+1)) % 2) == 0)
trickle_down(i, boost::mpl::true_());
else
trickle_down(i, boost::mpl::false_());
}
template <class IsMin>
void trickle_down(size_t pos, IsMin is_min)
{
size_t i = pos;
while (true)
{
size_t m = 2*i+1;
if (m >= _queue.size())
break;
for (size_t j = 2*i+1; j < 2*i+3; ++j)
{
if (j >= _queue.size())
break;
if (cmp(j, m, is_min))
m = j;
for (size_t l = 2*j+1; l < 2*j+3; ++l)
{
if (l >= _queue.size())
break;
if (cmp(l, m, is_min))
m = l;
}
}
if (m > 2*i+2) // is grandchild
{
if (cmp(m, i, is_min))
{
swap(_queue[m], _queue[i]);
if (!cmp(m, (m-1)/2, is_min))
swap(_queue[m], _queue[(m-1)/2]);
i = m;
}
else
{
break;
}
}
else
{
if (cmp(m, i, is_min))
swap(_queue[m], _queue[i]);
break;
}
}
}
void bubble_up(size_t i)
{
if ((size_t(log2(i+1)) % 2) == 0)
{
if ((i > 0) && cmp(i, (i-1)/2, boost::mpl::false_()))
{
swap(_queue[i], _queue[(i-1)/2]);
bubble_up((i-1)/2, boost::mpl::false_());
}
else
{
bubble_up(i, boost::mpl::true_());
}
}
else
{
if ((i > 0) && cmp(i, (i-1)/2, boost::mpl::true_()))
{
swap(_queue[i], _queue[(i-1)/2]);
bubble_up((i-1)/2, boost::mpl::true_());
}
else
{
bubble_up(i, boost::mpl::false_());
}
}
}
template <class IsMin>
void bubble_up(size_t pos, IsMin is_min)
{
size_t i = pos;
while (true)
{
if (i > 2) // has grandparent
{
size_t m = ((i-1)/2 - 1)/2;
if (cmp(i, m, is_min))
{
swap(_queue[m], _queue[i]);
i = m;
}
else
{
break;
}
}
else
{
break;
}
}
}
private:
std::vector<Value> _queue;
Compare _cmp;
};
#endif // MINMAX_HH
src/graph_tool/centrality/__init__.py
View file @ f12d5416
......@@ -390,10 +390,10 @@ def eigentrust(g, trust_map, vprop=None, norm=False, epslon=1e-6, max_iter=0,
else:
return vprop
def absolute_trust(g, trust_map, source=None, vprop=None, n_iter=100,
reversed=False, seed=None):
def absolute_trust(g, trust_map, source=None, vprop=None, epsilon=1e-6,
n_paths=100000, reversed=False):
r"""
Samples the absolute trust centrality of each vertex in the graph, or only
Calculate the absolute trust centrality of each vertex in the graph, or only
for a given source, if one is provided.
Parameters
......@@ -408,16 +408,15 @@ def absolute_trust(g, trust_map, source=None, vprop=None, n_iter=100,
values, instead of all the vertices in the graph.
vprop : PropertyMap, optional (default: None)
Vertex property map where the values of eigentrust must be stored.
n_iter : int, optional (default: 100)
Number of iterations (independent self-avoiding walks) per source
vertex.
epsilon : float, optional (default: 1e-6)
Convergence criterion for the algorithm. If the summed difference of
trust values drops below this value, the algorithm stops.
n_paths : int, optimal (default: 100000)
Maximum number of paths to keep following at any given time.
reversed : bool, optional (default: False)
Calculates the "reversed" trust instead: The direction of the edges are
inverted, but the path weighting is preserved in the original direction
(see Notes below).
seed : int, optional (default: None)
The initializing seed for the random number generator. If not supplied
a different random value will be chosen each time.
Returns
-------
......@@ -455,14 +454,11 @@ def absolute_trust(g, trust_map, source=None, vprop=None, n_iter=100,
such that the direct trust of the last edge on the path is not considered.
The algorithm performs only an approximation of the above measure, by doing
several self-avoiding random walks per source vertex, and computing the
trust for all different paths found. Each complete walk is done by one of
three types of biased choices, which govern the probability of following a
specific edge: 1. With probability proportional to an edge's trust value;
2. With probability proportional to the complement of an edge's trust value
:math:`(1-t_{e})`; 3. All edges with equal probability. The parameter
"n_iter" controls how many walks of each type are performed.
The algorithm measures the absolute trust by following all vertex-disjoint
paths, and keeping them on a priority queue. Each iteration the path with