Finding maximal bicliques in bipartite networks using node similarity

Alzahrani, T and Horadam, K 2019, 'Finding maximal bicliques in bipartite networks using node similarity', Applied Network Science, vol. 4, pp. 1-25.


Document type: Journal Article
Collection: Journal Articles

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Title Finding maximal bicliques in bipartite networks using node similarity
Author(s) Alzahrani, T
Horadam, K
Year 2019
Journal name Applied Network Science
Volume number 4
Start page 1
End page 25
Total pages 25
Publisher SpringerOpen
Abstract In real world complex networks, communities are usually both overlapping and hierarchical. A very important class of complex networks is the bipartite networks. Maximal bicliques are the strongest possible structural communities within them. Here we consider overlapping communities in bipartite networks and propose a method that detects an order-limited number of overlapping maximal bicliques covering the network. We formalise a measure of relative community strength by which communities can be categorised, compared and ranked. There are very few real bipartite datasets for which any external ground truth about overlapping communities is known. Here we test three such datasets. We categorise and rank the maximal biclique communities found by our algorithm according to our measure of strength. Deeper analysis of these bicliques shows they accord with ground truth and give useful additional insight. Based on this we suggest our algorithm can find true communities at the first level of a hierarchy. We add a heuristic merging stage to the maximal biclique algorithm to produce a second level hierarchy with fewer communities and obtain positive results when compared with other overlapping community detection algorithms for bipartite networks.
Subject Pattern Recognition and Data Mining
Applied Discrete Mathematics
Keyword(s) Bipartite network
Overlapping community detection
Maximal biclique
Community strength
Node similarity
DOI - identifier 10.1007/s41109-019-0123-6
Copyright notice © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
ISSN 2364-8228
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