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Commit cfc67930 authored by Tiago Peixoto's avatar Tiago Peixoto
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Simplify and re-implement absolute_trust() 

Now use _best weights_ to in-neighbours to determine trust, to avoid path
multiplicity catastrophe.
parent 8f787a13
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Showing with 160 additions and 394 deletions
src/graph/centrality/graph_absolute_trust.cc
View file @ cfc67930
......@@ -28,21 +28,20 @@ using namespace std;
using namespace boost;
using namespace graph_tool;
void absolute_trust(GraphInterface& g, int64_t source, boost::any c,
boost::any t, size_t n_paths, size_t n_paths_vertex,
double epsilon, bool reversed)
void absolute_trust(GraphInterface& g, int64_t source, int64_t target,
boost::any c, boost::any t)
{
if (!belongs<edge_floating_properties>()(c))
throw ValueException("edge property must be of floating point value type");
if (!belongs<vertex_floating_vector_properties>()(t))
throw ValueException("vertex property must be of floating point vector value type");
if (!belongs<vertex_floating_properties>()(t))
throw ValueException("vertex property must be of floating point value type");
run_action<>()(g,
bind<void>(get_absolute_trust(), _1, g.GetVertexIndex(),
g.GetEdgeIndex(), g.GetMaxEdgeIndex(), source, _2,
_3, make_tuple(n_paths, n_paths_vertex, epsilon),
reversed), edge_floating_properties(),
vertex_floating_vector_properties())(c, t);
g.GetEdgeIndex(), g.GetMaxEdgeIndex(), source,
target, _2, _3),
edge_floating_properties(),
vertex_floating_properties())(c,t);
}
void export_absolute_trust()
......
src/graph_tool/centrality/__init__.py
View file @ cfc67930
......@@ -406,8 +406,7 @@ 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, n_paths=10000,
n_paths_vertex=10, epsilon = 0, reversed=False):
def absolute_trust(g, trust_map, source, target = None, vprop=None):
r"""
Calculate the absolute trust centrality of each vertex in the graph, from a
given source.
......@@ -419,29 +418,23 @@ def absolute_trust(g, trust_map, source = None, vprop=None, n_paths=10000,
trust_map : :class:`~graph_tool.PropertyMap`
Edge property map with the values of trust associated with each
edge. The values must lie in the range [0,1].
source : Vertex (optional, default: None)
A vertex which is used the as the source for gathering trust values. If
left unspecified, the trust values for all sources are computed.
source : Vertex
A vertex which is used the as the source for gathering trust values.
target : Vertex (optional, default: None)
A vertex which is used the as the only target for which the trust value
will be calculated. If left unspecified, the trust values for all
targets are computed.
vprop : :class:`~graph_tool.PropertyMap`, optional (default: None)
Vector vertex property map where the values of trust for each source
A 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.
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).
Returns
-------
absolute_trust : :class:`~graph_tool.PropertyMap`
absolute_trust : :class:`~graph_tool.PropertyMap` or float
A vertex property map containing the absolute trust vector from the
corresponding vertex to the rest of the network. Each element i of the
vector is the trust value of the vertex with index i, from the given
vertex.
If the parameter "source" is specified, the values of the
property map are scalars, instead of vectors.
source vertex to the rest of the network. If `target` is specified, the
result is a single float, with the corresponding trust value for the
target.
See Also
--------
......@@ -455,28 +448,23 @@ def absolute_trust(g, trust_map, source = None, vprop=None, n_paths=10000,
.. math::
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
t_{ij} = \frac{\sum_m A_{m,j} w^2_{G\setminus\{j\}}(i\to m)c_{m,j}}
{\sum_m A_{m,j} w_{G\setminus\{j\}}(i\to m)}
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
where :math:`A_{ij}` is the adjacency matrix, :math:`c_{ij}` is the direct
trust from i to j, and :math:`w_G(i\to j)` is the weight of the path with
maximum weight from i to j, computed as
.. 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}},
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.
w_G(i\to j) = \prod_{e\in i\to j} c_e.
The algorithm measures the absolute trust by following all vertex-disjoint
paths, and keeping them on a priority queue. Each iteration the path with
maximum weight is augmented, and the new paths pushed into the queue. The
algorithm stops when all paths are consumed, or when the all the ``n_paths``
paths with largest weights are found.
The algorithm measures the absolute trust by finding the paths with maximum
weight, using Dijkstra's algorithm, to all in-neighbours of a given
target. This search needs to be performed repeatedly for every target, since
it needs to be removed from the graph first. The resulting complexity is
therefore :math:`O(N^2\log N)` for all targets, and :math:`O(N\log N)` for a
single target.
If enabled during compilation, this algorithm runs in parallel.
......@@ -486,70 +474,42 @@ def absolute_trust(g, trust_map, source = None, vprop=None, n_paths=10000,
>>> seed(42)
>>> g = gt.random_graph(100, lambda: (poisson(3), poisson(3)))
>>> trust = g.new_edge_property("double")
>>> trust.get_array()[:] = random(g.num_edges())
>>> trust.a = random(g.num_edges())
>>> t = gt.absolute_trust(g, trust, source=g.vertex(0))
>>> print t.a
[ 0.00000000e+00 5.14258135e-02 2.42874582e-04 1.05347472e-06
0.00000000e+00 3.13429149e-04 1.53697222e-04 3.83063399e-05
2.65668937e-06 2.04029901e-05 1.19582153e-05 2.67743821e-06
1.50606560e-04 1.51595650e-05 5.72684475e-05 2.16466381e-06
0.00000000e+00 4.08340061e-05 3.26896572e-06 7.80860267e-05
7.31033290e-05 7.81690832e-05 2.93440658e-04 1.19013202e-05
1.60601849e-06 6.79167712e-05 9.35414301e-05 1.98991248e-05
2.08142130e-05 1.28565785e-04 2.83893891e-03 8.45362053e-05
1.15751883e-05 1.97248846e-05 0.00000000e+00 7.51004486e-06
5.49704676e-07 0.00000000e+00 1.06219388e-04 9.64852468e-04
0.00000000e+00 4.70496027e-05 5.49108602e-05 6.23617670e-06
1.32625806e-06 7.35202433e-05 2.09546902e-06 1.99138155e-03
4.32934771e-06 2.61887887e-05 2.55099939e-05 3.90874553e-06
9.07765143e-05 2.59243068e-06 7.50032403e-06 8.36211398e-05
7.80814352e-04 8.12133072e-06 6.24066931e-04 2.19465770e-06
4.15039190e-05 5.41464668e-05 1.84421073e-03 8.02449156e-06
4.01472852e-06 3.76746767e-01 7.02886863e-05 1.52365123e-04
4.58687938e-06 3.70470973e-02 0.00000000e+00 1.85922960e-06
2.05481272e-05 1.41021895e-04 1.45217040e-06 3.18562543e-06
2.62264044e-01 7.41140347e-06 1.39150089e-05 3.86583428e-06
2.85681164e-06 4.12923146e-06 7.05705402e-07 2.12584322e-05
1.65948868e-04 3.10144404e-05 5.08749580e-06 0.00000000e+00
1.45435603e-03 4.19224443e-03 4.88198531e-05 3.00152848e-04
5.61591759e-05 2.31951396e-04 1.19051653e-05 2.34710286e-05
6.27636571e-04 1.65759606e-02 1.30944429e-05 1.26282526e-05]
[ 0.05927703 0.06133836 0. 0.05630559 0. 0.03317174
0.03488483 0.15920558 0.16940159 0.09716039 0.1485169 0.0120287
0.03787312 0.37284274 0.00646336 0.0084941 0.0379645 0.07997339
0.10733769 0.10053845 0.00283938 0.05224064 0. 0.16523684
0.0393326 0.25853808 0.14682555 0.03254906 0.12124144 0.0118341
0.18110839 0.18513216 0.05031324 0.04484457 0.17197674 0.08569659
0.17523371 0.22435776 0.33916191 0.07980329 0. 0.
0.09750183 0.09811054 0.14574289 0.0085499 0.34593499 0.03151408
0.083739 0.05409947 0.09161205 0.19921201 0.10647812 0.21597253
0.06266044 0.8738786 0.11239455 0.09493216 0.19073287 0.11968616
0.13409125 0.00626821 0.05857625 0.05917779 0.05673643 0.
0.02682173 0.00355514 0.17475858 0.15113517 0.13247358 0.
0.04003866 0.00997401 0.11126411 0.07400706 0.11247583 0.10125886
0.16028191 0.04300862 0.03259707 0.0225482 0.05538721 0.
0.06715919 0.0701153 0.02999368 0.04675702 0.06310919 0.01722603
0.18455906 0.08034113 0.00376382 0.10041304 0.3437539 0.10530238
0.11654855 0.09495419 0.05317485 0.10727767]
"""
if vprop == None:
if source == None:
vprop = g.new_vertex_property("vector<double>")
else:
vprop = g.new_vertex_property("double")
if source != None:
vprop_temp = vprop
vprop = g.new_vertex_property("vector<double>")
source = g.vertex_index[source]
else:
source = -1
if reversed:
g.stash_filter(reversed=True)
try:
if reversed:
g.set_reversed(True)
vprop = g.new_vertex_property("double")
libgraph_tool_centrality.\
get_absolute_trust(g._Graph__graph, source,
_prop("e", g, trust_map),
_prop("v", g, vprop), n_paths,
n_paths_vertex, epsilon, reversed)
finally:
if reversed:
g.pop_filter(reversed=True)
source = g.vertex_index[source]
if source != -1:
n = len(vprop[g.vertex(source)])
vprop_temp.a[:n] = numpy.array(vprop[g.vertex(source)])
vprop = vprop_temp
if target == None:
target = -1
else:
target = g.vertex_index[target]
libgraph_tool_centrality.\
get_absolute_trust(g._Graph__graph, source, target,
_prop("e", g, trust_map), _prop("v", g, vprop))
if target != -1:
return vprop.a[target]
return vprop
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