7 research outputs found
Inferring monopartite projections of bipartite networks: an entropy-based approach
Bipartite networks are currently regarded as providing a major insight into
the organization of many real-world systems, unveiling the mechanisms driving
the interactions occurring between distinct groups of nodes. One of the most
important issues encountered when modeling bipartite networks is devising a way
to obtain a (monopartite) projection on the layer of interest, which preserves
as much as possible the information encoded into the original bipartite
structure. In the present paper we propose an algorithm to obtain
statistically-validated projections of bipartite networks, according to which
any two nodes sharing a statistically-significant number of neighbors are
linked. Since assessing the statistical significance of nodes similarity
requires a proper statistical benchmark, here we consider a set of four null
models, defined within the exponential random graph framework. Our algorithm
outputs a matrix of link-specific p-values, from which a validated projection
is straightforwardly obtainable, upon running a multiple hypothesis testing
procedure. Finally, we test our method on an economic network (i.e. the
countries-products World Trade Web representation) and a social network (i.e.
MovieLens, collecting the users' ratings of a list of movies). In both cases
non-trivial communities are detected: while projecting the World Trade Web on
the countries layer reveals modules of similarly-industrialized nations,
projecting it on the products layer allows communities characterized by an
increasing level of complexity to be detected; in the second case, projecting
MovieLens on the films layer allows clusters of movies whose affinity cannot be
fully accounted for by genre similarity to be individuated.Comment: 16 pages, 9 figure
Anomalous dynamical scaling in anharmonic chains and plasma models with multiparticle collisions
We study the anomalous dynamical scaling of equilibrium correlations in one
dimensional systems. Two different models are compared: the Fermi-Pasta-Ulam
chain with cubic and quartic nonlinearity and a gas of point particles
interacting stochastically through the multiparticle collision dynamics. For
both models -that admit three conservation laws- by means of detailed numerical
simulations we verify the predictions of nonlinear fluctuating hydrodynamics
for the structure factors of density and energy fluctuations at equilibrium.
Despite this, violations of the expected scaling in the currents correlation
are found in some regimes, hindering the observation of the asymptotic scaling
predicted by the theory. In the case of the gas model this crossover is clearly
demonstrated upon changing the coupling constant.Comment: 12 pages, 8 figures. Matching the version published in Phys. Rev.
Inferring monopartite projections of bipartite networks: an entropy-based approach
Bipartite networks are currently regarded as providing a major insight into the organization of many real-world systems, unveiling the mechanisms driving the interactions occurring between distinct groups of nodes. One of the most important issues encountered when modeling bipartite networks is devising a way to obtain a (monopartite) projection on the layer of interest, which preserves as much as possible the information encoded into the original bipartite structure. In the present paper we propose an algorithm to obtain statistically-validated projections of bipartite networks, according to which any two nodes sharing a statistically-significant number of neighbors are linked. Since assessing the statistical significance of nodes similarity requires a proper statistical benchmark, here we consider a set of four null models, defined within the exponential random graph framework. Our algorithm outputs a matrix of link-specific p-values, from which a validated projection is straightforwardly obtainable, upon running a multiple hypothesis testing procedure. Finally, we test our method on an economic network (i.e. the countries-products World Trade Web representation) and a social network (i.e. MovieLens, collecting the users' ratings of a list of movies). In both cases non-trivial communities are detected: while projecting the World Trade Web on the countries layer reveals modules of similarly-industrialized nations, projecting it on the products layer allows communities characterized by an increasing level of complexity to be detected; in the second case, projecting MovieLens on the films layer allows clusters of movies whose affinity cannot be fully accounted for by genre similarity to be individuated