2,550 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
How to project a bipartite network?
The one-mode projecting is extensively used to compress the bipartite
networks. Since the one-mode projection is always less informative than the
bipartite representation, a proper weighting method is required to better
retain the original information. In this article, inspired by the network-based
resource-allocation dynamics, we raise a weighting method, which can be
directly applied in extracting the hidden information of networks, with
remarkably better performance than the widely used global ranking method as
well as collaborative filtering. This work not only provides a creditable
method in compressing bipartite networks, but also highlights a possible way
for the better solution of a long-standing challenge in modern information
science: How to do personal recommendation?Comment: 7 pages, 4 figure
The Impact of Projection and Backboning on Network Topologies
Bipartite networks are a well known strategy to study a variety of phenomena.
The commonly used method to deal with this type of network is to project the
bipartite data into a unipartite weighted graph and then using a backboning
technique to extract only the meaningful edges. Despite the wide availability
of different methods both for projection and backboning, we believe that there
has been little attention to the effect that the combination of these two
processes has on the data and on the resulting network topology. In this paper
we study the effect that the possible combinations of projection and backboning
techniques have on a bipartite network. We show that the 12 methods group into
two clusters producing unipartite networks with very different topologies. We
also show that the resulting level of network centralization is highly affected
by the combination of projection and backboning applied
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