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Commit ca4c120f authored by Tiago Peixoto's avatar Tiago Peixoto
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Modify absolute trust 

Now paths are weighted according to their trust value until the last
hop, and paths are not counted twice.
parent 95484cb5
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Inline Side-by-side
src/graph/centrality/graph_absolute_trust.hh
View file @ ca4c120f
......@@ -22,7 +22,9 @@
#include "graph_filtering.hh"
#include "graph_util.hh"
#include <tr1/unordered_set>
#include <boost/random/uniform_int.hpp>
#include <boost/functional/hash.hpp>
namespace graph_tool
{
......@@ -42,7 +44,7 @@ struct get_absolute_trust
typedef typename property_traits<InferredTrustMap>::value_type
::value_type t_type;
unchecked_vector_property_map<vector<size_t>, VertexIndex>
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));
......@@ -63,16 +65,20 @@ struct get_absolute_trust
v_mark[v].resize(N,0);
}
t_type delta = 2*epslon;
size_t iter = 0;
while (delta >= epslon)
#pragma omp parallel for default(shared) private(i) schedule(dynamic)
for (i = (source == -1) ? 0 : source;
i < ((source == -1) ? N : source + 1); ++i)
{
delta = 0;
#pragma omp parallel for default(shared) private(i) \
schedule(dynamic) reduction(+:delta)
for (i = (source == -1) ? 0 : source;
i < ((source == -1) ? N : source + 1); ++i)
// walk hash set
tr1::unordered_set<size_t> path_set;
uniform_int<size_t> random_salt(0, numeric_limits<size_t>::max());
size_t salt = random_salt(rng);
t_type delta = 2*epslon;
size_t iter = 0;
while (delta >= epslon || iter < 10)
{
delta = 0;
typename graph_traits<Graph>::vertex_descriptor v =
vertex(i, g);
if (v == graph_traits<Graph>::null_vertex())
......@@ -82,6 +88,9 @@ struct get_absolute_trust
t_type pos_t = 1.0;
v_mark[v][vertex_index[v]] = 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;
......@@ -124,23 +133,32 @@ struct get_absolute_trust
pos = target(e,g);
size_t posi = vertex_index[pos];
//update current path trust, and update new vertex
pos_t *= c[e];
t_type old = 0;
if (t_count[v][posi] > 0)
old = t[v][posi]/t_count[v][posi];
t[v][posi] += pos_t;
t_count[v][posi]++;
delta += abs(old - t[v][posi]/t_count[v][posi]);
// get path hash
hash_combine(path_hash, posi);
if (path_set.find(path_hash) == path_set.end())
{
path_set.insert(path_hash);
//update current path trust, and update new vertex
t_type pweight = pos_t;
pos_t *= c[e];
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;
delta += abs(old - t[v][posi]/t_count[v][posi]);
}
v_mark[v][posi] = iter+1; // mark vertex
}
}
while (!out_es.empty());
}
++iter;
if (max_iter > 0 && iter == max_iter)
break;
++iter;
if (max_iter > 0 && iter == max_iter)
break;
}
}
#pragma omp parallel for default(shared) private(i) \
......
src/graph_tool/centrality/__init__.py
View file @ ca4c120f
......@@ -379,7 +379,7 @@ 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, epslon=0.1,
def absolute_trust(g, trust_map, source=None, vprop=None, epslon=0.001,
max_iter=None, seed=None, ret_iter=False):
r"""
Samples the absolute trust centrality of each vertex in the graph, or only
......@@ -397,7 +397,7 @@ def absolute_trust(g, trust_map, source=None, vprop=None, epslon=0.1,
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.
epslon : float, optional (default: 0.1)
epslon : float, optional (default: 0.001)
Convergence condition. The iteration will stop if the total delta of all
vertices are below this value.
max_iter : int, optional (default: None)
......@@ -428,12 +428,17 @@ def absolute_trust(g, trust_map, source=None, vprop=None, epslon=0.1,
The absolute trust between vertices i and j is defined as
.. math:
t_{ij} = \frac{1}{|\{i\to j\}|}\sum_{\{i\to j\}}
\prod_{e\in \{i\to j\}}
\frac{c_e^2}{\sum_{w\in\Gamma^+(s(e))}c_{s(e),w}}}
t_{ij} = \frac{1}{\sum_{\{i\to j\}}w_{\{i\to j\}}}\sum_{\{i\to j\}}
w_{\{i\to j\}} \prod_{e\in \{i\to j\}}c_e
where the sum is taken over all paths from i to j (without loops), and
:math:`c_e` is the direct trust value associated with edge e.
where the sum is taken over all paths from i to j (without loops),
:math:`c_e` is the direct trust value associated with edge e, and
:math:`w_{\{i\to j\}}` is the weight of a given path, which is defined as
.. math:
w_{\{i\to j\}} = \prod_{e\in \{i\to j\}}\{\delta_{t(e),j}(1-c_e) + c_e\},
such that the direct trust of the last edge on the path is not considered.
The algorithm progressively samples all possible paths, until the trust
values converge, and has a topology-dependent complexity.
......@@ -447,25 +452,33 @@ def absolute_trust(g, trust_map, source=None, vprop=None, epslon=0.1,
>>> g = gt.random_graph(100, lambda: (poisson(3), poisson(3)), seed=42)
>>> trust = g.new_edge_property("double")
>>> trust.get_array()[:] = random(g.num_edges())
>>> t = absolute_trust(g, trust)
>>> t = gt.absolute_trust(g, trust)
>>> print array(t[g.vertex(10)])
[ 0.00452395 0.00358993 0.00520913 0.00151395 0.09479413 0.00431631
0.09957709 0.00722076 0.02488298 0.02720262 0. 0.02958085
0.05583483 0.00525581 0.02112018 0.00157646 0.02070552 0.01317581
0.01565533 0.00568109 0.04568674 0.00202402 0.0024926 0.14040174
0.0093484 0.00124116 0.009818 0.039403 0.00787983 0.0130681
0.02046159 0.02044219 0.00625258 0.00253353 0.00992648 0.00658357
0.00328796 0.05730617 0.00752433 0.00289023 0. 0.01610246
0.03151005 0.05449376 0.0195204 0.00296101 0.0187164 0.19553864
0.01089019 0.01516855 0.01621888 0.29711525 0.00164373 0.02045437
0.01388174 0.00109321 0.03034565 0.00289681 0.06903929 0.02392237
0.01491933 0.02128263 0.03091464 0.03457097 0.14454613 0.01821371
0.00943718 0.0247563 0.00495901 0.03532278 0.00053465 0.
0.00142457 0.03393286 0.0058909 0.01881276 0.00156345 0.00878983
0.00832669 0.08389869 0.43991565 0.04075081 0.00323008 0.02823037
0.03224312 0.00430044 0.0331929 0.00268128 0.01462425 0.00720545
0.06730403 0.02771813 0.03289217 0.01326689 0.06876157 0.02382899
0.1502834 0.00980331 0.0086688 0.00495706]
[ 1.98147630e-01 3.86048721e-01 2.15038631e-01 6.35971261e-01
3.70028165e-01 8.24462513e-01 1.87542375e-04 2.40775039e-03
6.63467272e-01 1.30124153e-02 0.00000000e+00 2.55161924e-01
6.11894792e-01 1.64132684e-01 1.08372073e-01 3.58317237e-01
7.05106201e-02 2.48412716e-07 4.28006145e-01 8.59461489e-04
0.00000000e+00 0.00000000e+00 7.25301232e-01 1.01773307e-01
3.16379391e-01 2.53316731e-01 1.59819436e-08 3.70296181e-01
1.57203884e-01 0.00000000e+00 7.87247979e-01 2.18598076e-04
5.52859606e-01 1.24994552e-01 4.20286331e-02 4.15065578e-01
6.43653877e-01 3.24950468e-01 7.38208378e-01 7.29509079e-08
1.93750752e-01 7.65610195e-01 3.36921596e-01 6.57309628e-01
9.52773935e-02 8.03124227e-03 1.30578359e-02 6.88776780e-01
1.73090783e-04 4.82732890e-01 6.26331031e-12 5.35175859e-01
1.47736985e-01 1.11789227e-01 2.73859867e-01 5.64369671e-01
4.18831134e-01 1.98422984e-15 3.58564257e-01 1.27871795e-01
4.29322288e-01 1.42919207e-05 3.02946673e-01 3.90436999e-01
2.89626434e-01 1.34307383e-01 3.11974410e-01 3.70614146e-01
5.33011347e-02 3.81536049e-02 1.01675425e-01 1.45882725e-02
3.53278685e-02 5.43455032e-03 6.52215036e-01 3.61707159e-01
6.08608841e-02 8.96019737e-04 2.60395071e-02 0.00000000e+00
1.86921647e-01 2.49218885e-01 8.22384329e-01 6.75209818e-03
5.27060698e-01 1.34291381e-02 3.25840921e-13 7.88987646e-02
2.20961189e-03 4.97124982e-01 0.00000000e+00 1.00335219e-02
5.50608327e-01 1.65947138e-04 0.00000000e+00 1.32775697e-03
6.21862966e-01 5.42485152e-01 5.41292375e-08 3.73524878e-01]
"""
if seed != 0:
......
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