Module identification in bipartite and directed networks.

By Roger Guimerà, Marta Sales-Pardo, Luís A. Nunes Amaral. Physical review. E, Statistical, nonlinear, and soft matter physics, 2007

417 citations recorded by the source corpus.

Modularity is one of the most prominent properties of real-world complex networks. Here, we address the issue of module identification in two important classes of networks: bipartite networks and directed unipartite networks. Nodes in bipartite networks are divided into two nonoverlapping sets, and the links must have one end node from each set. Directed unipartite networks only have one type of node, but links have an origin and an end. We show that directed unipartite networks can be conveniently represented as bipartite networks for module identification purposes. We report on an approach especially suited for module detection in bipartite networks, and we define a set of random networks that enable us to validate the approach.

Subfields: Geometry and Topology, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/518821992339096996b9915fac26eb2130d2ef62

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Geometry and Topology

In Molecular Biology

In Statistical and Nonlinear Physics

Citations in the corpus

Motif-based communities in complex networks (2007, background): “We remark that bipartite networks are characterized by the fact that any path with even length starting from a node of either class ends in the same class, due to the absence of internal edges in each class.”
Networks beyond pairwise interactions: structure and dynamics (2020, background): “This representation emerges naturally in many fields: it is used for example in social sciences, where it provides a way to encode the membership of individuals to groups of different dimensions [46, 47]; or to describe the collaboration of actors (nodes) in movies (interactions) [48]; it is also us”
Uncovering space-independent communities in spatial networks (2010, background): “P P Beyond these basic considerations, diﬀerent null models can be constructed depending on the network under consideration [59, 60, 61].”
Clustering and Community Detection in Directed Networks: A Survey (2013, methodology): “[66, 57] utilize a transformation scheme where a bipartite graph GB = (Vh, Va, Eb) is constructed from the original directed network G = (V,E), according to the following process:”
Community structure in directed networks. (2007, background): “Several previous studies, including our own, have touched on this problem in the context of other analyses of directed network data [6, 7, 8, 9], but they have typically not tackled the community structure problem directly.”
Identifying compartments in presence–absence matrices and bipartite networks: insights into modularity measures (2013, methodology): “Although two modularity measures have recently been proposed for bipartite networks (Barber, 2007; Guimerà et al., 2007), these measures have not been tested in ecology.”
Community Structure in Time-Dependent, Multiscale, and Multiplex Networks (2009, neutral): “ijsr {(Aijs − γsδsr) + δijCjsr} δ(cis, cjr) (12)”
Supporting Online Material for Evolutionary Conservation of Species' Roles in Food Webs (2012, methodology): “In order to group species according to their motif prof les, we extended previous community detection algorithms for complex networks (22,23,58) to the case of detecting groups of nodes in bipartite graphs with weighted edges.”
Improved community detection in weighted bipartite networks (2016, background): “Guimerà’s modularity [8] and Barber’s modularity [7] were recently reviewed [9] in the context of ecological networks.”
Community detection in graphs (2009, background): “Simulated annealing was first employed for modularity optimization by Guimerà et al. (Guimerà et al., 2004).”
Complex network approaches to nonlinear time series analysis (2019, background): “Bipartite networks can be found in a wide range of fields [193,194] and can be understood as a generic way for describing arbitrary complex networks [195,196].”