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Commit 10dd7be8 authored by Tiago Peixoto's avatar Tiago Peixoto
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Revert weight calculation in absolute_trust() 

Use the original, simpler, path weight calculation.
parent 6d1810e1
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Showing with 40 additions and 147 deletions
src/graph/centrality/graph_absolute_trust.hh
View file @ 10dd7be8
......@@ -89,14 +89,12 @@ struct get_absolute_trust
get<1>(paths.back()).insert(v);
queue.push(0);
// this is the actual queue of paths which will be used to compute
// the trust values
// this is the queue with paths with maximum weights
queue_t final_queue = queue_t(path_cmp<path_t>(paths));
// store all paths which reach a given vertex
unchecked_vector_property_map<tr1::unordered_set<size_t>,
VertexIndex>
path_map(vertex_index, num_vertices(g));
// sum of weights that lead to a given vertex
unchecked_vector_property_map<t_type, VertexIndex>
weight_sum(vertex_index, num_vertices(g));
while (!queue.empty())
{
......@@ -109,9 +107,6 @@ struct get_absolute_trust
{
w = target(get<2>(paths[pi]).back(), g);
final_queue.push(pi);
// augment path map
path_map[target(get<2>(paths[pi]).back(),g)].insert(pi);
}
else
{
......@@ -124,10 +119,6 @@ struct get_absolute_trust
size_t bi = final_queue.bottom();
final_queue.pop_bottom();
// remove path from path map
path_map[boost::target(get<2>(paths[bi]).back(),g)].
erase(bi);
if (bi == pi)
continue;
}
......@@ -140,139 +131,42 @@ struct get_absolute_trust
// no loops
if (get<1>(paths[pi]).find(a) == get<1>(paths[pi]).end())
{
// only follow non-zero paths
if (c[*e] > 0 || (reversed &&
get<1>(paths[pi]).size() == 1))
{
size_t npi;
paths.push_back(paths[pi]); // clone last path
npi = paths.size()-1;
path_t& np = paths[npi]; // new path
if (!reversed)
{
// path weight
get<0>(np).second = get<0>(np).first;
// path value
get<0>(np).first *= c[*e];
}
else
{
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).push_back(*e);
// keep following paths only if there is a chance
// they will make it into the final queue
if ((n_paths > 0 && final_queue.size() < n_paths) ||
(final_queue.size() == 0 ||
(get<0>(np).second >=
get<0>(paths[final_queue.bottom()]).second)))
queue.push(npi);
else
paths.pop_back();
}
}
}
}
unchecked_vector_property_map<t_type, VertexIndex>
weight_sum(vertex_index, num_vertices(g));
// paths which were already calculated and can be skipped
tr1::unordered_set<size_t> skip;
// calculate trust from paths in the final queue
while (!final_queue.empty())
{
size_t pi = final_queue.top();
final_queue.pop_top();
path_t& p = paths[pi];
size_t npi;
paths.push_back(paths[pi]); // clone last path
npi = paths.size()-1;
if (skip.find(pi) != skip.end())
continue;
// mark edges which are to be considered
unchecked_vector_property_map<uint8_t, EdgeIndex>
mark(edge_index, max_edge_index+1);
tr1::unordered_set<size_t>& apaths =
path_map[target(get<2>(p).back(), g)]; // all paths with the
// same final target
tr1::unordered_set<size_t> vlist; // all vertices involved
for (typeof(apaths.begin()) iter = apaths.begin();
iter != apaths.end(); ++iter)
{
for (size_t j = 0; j < get<2>(paths[*iter]).size(); ++j)
{
edge_t e = get<2>(paths[*iter])[j];
mark[e] = 1;
vlist.insert(target(e,g));
}
vlist.insert(boost::source(get<2>(paths[*iter]).front(),g));
}
path_t& np = paths[npi]; // new path
// compute out trust
unchecked_vector_property_map<t_type, VertexIndex>
out_trust(vertex_index, num_vertices(g));
for (typeof(vlist.begin()) viter = vlist.begin();
viter != vlist.end(); ++viter)
{
vertex_t u = *viter;
if (!reversed)
{
typename graph_traits<Graph>::out_edge_iterator e,e_end;
for (tie(e, e_end) = out_edges(u, g); e != e_end; ++e)
if (mark[*e] == 1)
out_trust[u] += c[*e];
}
else
{
// if reversed, use "in-trust" instead
typename in_edge_iteratorS<Graph>::type e, e_end;
for (tie(e, e_end) =
in_edge_iteratorS<Graph>::get_edges(v, g);
e != e_end; ++e)
if (!reversed)
{
if (mark[*e] == 1)
out_trust[u] += c[*e];
}
}
}
// path weight
get<0>(np).second = get<0>(np).first;
for (typeof(apaths.begin()) iter = apaths.begin();
iter != apaths.end(); ++iter)
{
size_t pi = *iter;
path_t& p = paths[pi];
// calculate the trust value and weight of the path
t_type w = 1, val = 1;
for (size_t i = 0; i < get<2>(p).size(); ++i)
{
edge_t e = get<2>(p)[i];
vertex_t u = (!reversed) ?
boost::source(e,g) : target(e,g);
if (out_trust[u] > 0)
// path value
get<0>(np).first *= c[*e];
}
else
{
if ((!reversed && i < get<2>(p).size()-1) ||
(reversed && i > 0))
w *= c[e]*c[e]/out_trust[u];
else
w *= c[e]/out_trust[u];
if (get<1>(np).size() > 1)
get<0>(np).second *= c[*e];
get<0>(np).first *= c[*e];
}
val *= c[e];
weight_sum[a] += get<0>(np).second;
t[v][a] += get<0>(np).second*get<0>(np).first;
get<1>(np).insert(a);
get<2>(np).push_back(*e);
// keep following paths only if there is a chance
// they will make it into the final queue
if ((n_paths > 0 && final_queue.size() < n_paths) ||
(final_queue.size() == 0 ||
(get<0>(np).second >=
get<0>(paths[final_queue.bottom()]).second)))
queue.push(npi);
else
paths.pop_back();
}
vertex_t u = target(get<2>(p).back(), g);
weight_sum[u] += w;
t[v][u] += w*val;
skip.insert(pi);
}
}
......@@ -281,11 +175,11 @@ struct get_absolute_trust
schedule(dynamic)
for (j = 0; j < N; ++j)
{
vertex_t w = vertex(i, g);
vertex_t w = vertex(j, g);
if (w == graph_traits<Graph>::null_vertex())
continue;
if (weight_sum[w] > 0)
t[v][w] /= weight_sum[v];
t[v][w] /= weight_sum[w];
}
}
}
......
src/graph_tool/centrality/__init__.py
View file @ 10dd7be8
......@@ -426,7 +426,8 @@ def absolute_trust(g, trust_map, source = None, vprop=None, n_paths=10000,
Vector vertex property map where the values of trust for each source
must be stored.
n_paths : int, optimal (default: 10000)
Maximum number of paths to consider.
Number of paths (per source vertex) with largest weights to
consider. When all these paths have been found, the algorithm stops.
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
......@@ -464,13 +465,11 @@ def absolute_trust(g, trust_map, source = None, vprop=None, n_paths=10000,
.. math::
w_{\{i\to j\}} = \prod_{e\in \{i\to j\}}\frac{c_e}{\Gamma^+_{\{i\to j\}}(s(e))}
\{c_e(1-\delta_{t(e),j}) + \delta_{t(e),j}},
w_{\{i\to j\}} = \prod_{e\in \{i\to j\}}\{c_e(1-\delta_{t(e),j}) +
\delta_{t(e),j}},
such that the direct trust of the last edge on the path is not
considered. The value :math:`\Gamma^+_{\{i\to j\}}(s(e))` is the sum of
trust values of the selected out-edges of vertex :math:`s(e)`, which also
belong to the set of edge-disjoint of paths from i to j.
considered.
The algorithm measures the absolute trust by following all vertex-disjoint
paths, and keeping them on a priority queue. Each iteration the path with
......
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