Improved community detection in weighted bipartite networks

By Stephen J. Beckett. Royal Society Open Science, 2016

477 citations recorded by the source corpus.

Real-world complex networks are composed of non-random quantitative interactions. Identifying communities of nodes that tend to interact more with each other than the network as a whole is a key research focus across multiple disciplines, yet many community detection algorithms only use information about the presence or absence of interactions between nodes. Weighted modularity is a potential method for evaluating the quality of community partitions in quantitative networks. In this framework, the optimal community partition of a network can be found by searching for the partition that maximizes modularity. Attempting to find the partition that maximizes modularity is a computationally hard problem requiring the use of algorithms. QuanBiMo is an algorithm that has been proposed to maximize weighted modularity in bipartite networks. This paper introduces two new algorithms, LPAwb+ and DIRTLPAwb+, for maximizing weighted modularity in bipartite networks. LPAwb+ and DIRTLPAwb+ robustly identify partitions with high modularity scores. DIRTLPAwb+ consistently matched or outperformed QuanBiMo, while the speed of LPAwb+ makes it an attractive choice for detecting the modularity of larger networks. Searching for modules using weighted data (rather than binary data) provides a different and potentially insightful method for evaluating network partitions.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b24b5888e56554d87d8bb7106ea9610b9e536b4d

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In Statistical and Nonlinear Physics

Citations in the corpus

Temporal scale‐dependence of plant–pollinator networks (2020, methodology): “We calculated weighted quantitative modularity Q using the DIRTLPA+ algorithm (Beckett 2016).”
Plant-hummingbird interaction networks in urban areas: Generalization and the importance of trees with specialized flowers as a nectar resource for pollinator conservation (2019, methodology): “Lastly, modularity quanti ﬁ es the prevalence of interactions within modules in relation to interactions between modules, and it was calculated using the DIRTLPAwb+ algorithm (Beckett, 2016).”