In contrast to traditional social networks, signed ones encode both relations of
affinity and disagreement. Community discovery in this kind of networks has
been successfully addressed using the Potts model, originated in statistical mechanics
to explain the magnetic dipole moments of atomic spins. However, due
to the computational complexity of finding an exact solution, it has not been applied
to many real-world networks yet. We propose a novel approach to compute
an approximated solution ...
In contrast to traditional social networks, signed ones encode both relations of
affinity and disagreement. Community discovery in this kind of networks has
been successfully addressed using the Potts model, originated in statistical mechanics
to explain the magnetic dipole moments of atomic spins. However, due
to the computational complexity of finding an exact solution, it has not been applied
to many real-world networks yet. We propose a novel approach to compute
an approximated solution to the Potts model applied to the context of community
discovering, which is based on a continuous convex relaxation of the original problem
using hinge-loss functions. We show empirically the benefits of the proposed
method in comparison with loopy belief propagation in terms of the communities
discovered. We illustrate the scalability and effectiveness of our approach by applying
it to the network of voters of the European Parliament that we have crawled
for this study. This large-scale and dense network comprises about 300 votings periods
on the actual term involving a total of more than 730 voters. Remarkably,
the two major communities are those created by the european-antieuropean antagonism,
rather than the classical right-left antagonism.
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