Basic notions for the analysis of large two-mode networks

By Matthieu Latapy, Clémence Magnien, N. Vecchio. Soc. Networks, 2008

634 citations recorded by the source corpus.

Many large real-world networks actually have a two-mode nature: their nodes may be separated into two classes, the links being between nodes of different classes only. Despite this, and despite the fact that many ad hoc tools have been designed for the study of special cases, very few exist to analyse (describe, extract relevant information) such networks in a systematic way. We propose here an extension of the most basic notions used nowadays to analyse large one-mode networks (the classical case) to the two-mode case. To achieve this, we introduce a set of simple statistics, which we discuss by comparing their values on a representative set of real-world networks and on their random versions. This makes it possible to evaluate their relevance in capturing properties of interest in two-mode networks.

Subfields: Geometry and Topology, Mathematical Physics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/d4aad21797f84c5b9043c3525dfd321999a0484e

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Citations in the corpus

The statistical physics of real-world networks (2018, background): “The problem of building a statistically validated projection of a bipartite network is similar in spirit to that of extracting the backbone from standard weighted networks [80–83], and has been typically addressed by determining which links are significant using a threshold— either unconditional or ”
Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics (2016, background): “In bigraphs, sometimes also referred to as affiliation or two-mode networks (Borgatti and Everett 1997; Breiger 1974; Latapy et al. 2008), there are two types of nodes with edges only between nodes of different types.”
A topological insight into restricted Boltzmann machines (2016, methodology): “To confirm the previous results on a different (more complicated) database, further on we assess XBMs generative performance on the CalTech 101 Silhouettes database (Marlin et al, 6 The average cluster coefficient was computed using the method proposed for bipartite graphs in Latapy et al (2008).”
Networks beyond pairwise interactions: structure and dynamics (2020, background): “That said, there have since been quite a few explicit treatments of the bipartite case, coming mainly from social network literature where bipartite networks are known as “two-mode data” [165].”
Systems Toxicology of Male Reproductive Development: Profiling 774 Chemicals for Molecular Targets and Adverse Outcomes (2015, positive): “The concept of bipartite networks (Fuxman Bass et al. 2013) has been used in the functional annotation of protein interaction networks (Kelley and Ideker 2005; Sharan et al. 2007) and in statistical modeling of complex social networks (Easley and Kleinberg 2010; Latapy et al. 2008).”
Hypergraphs and Cellular Networks (2009, background): “…an actor–movie hypergraph obeys power-law distributions in both degrees whereas an author–publication hypergraph shows a power law only in the number of co-authored papers, but not in the author degree [30]—which is simply due to the fact that the number of authors on a paper is relatively limited.”
Triadic closure in two-mode networks: Redefining the global and local clustering coefficients (2010, methodology): “fficient in corresponding one-mode classical random networks. To overcome this bias, a number of clustering coefficients for two-mode networks have been proposed in the literature (Lind et al., 2005; Latapy et al., 2008; Robins and Alexander, 2004; Zhang et al., 2008). These measures focus on 4-cycl”
Exponential random graph models for multilevel networks (2013, methodology): “For bipartite analysis, ERGMs, SAOMs, and other methods are available (e.g. Latapy et al., 2008; Wang et al., 2009, 2013; Koskinen and Elding, 2010).”