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Is it correct to project and detect? How weighting unipartite projections influences community detection

Published online by Cambridge University Press:  17 April 2020

Tristan J. B. Cann Open the ORCID record for Tristan J. B. Cann [Opens in a new window] ,
Iain S. Weaver  and
Hywel T. P. Williams
Tristan J. B. Cann*
Affiliation:
College of Engineering, Mathematics and Physical Sciences, Harrison Building, Streatham Campus, University of Exeter, North Park Road, Exeter, EX4 4QF, UK (e-mails: [email protected], [email protected])
Iain S. Weaver
Affiliation:
College of Engineering, Mathematics and Physical Sciences, Harrison Building, Streatham Campus, University of Exeter, North Park Road, Exeter, EX4 4QF, UK (e-mails: [email protected], [email protected])
Hywel T. P. Williams
Affiliation:
College of Engineering, Mathematics and Physical Sciences, Harrison Building, Streatham Campus, University of Exeter, North Park Road, Exeter, EX4 4QF, UK (e-mails: [email protected], [email protected])
*
*Corresponding author. Email: [email protected]
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Abstract

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Bipartite networks represent pairwise relationships between nodes belonging to two distinct classes. While established methods exist for analyzing unipartite networks, those for bipartite network analysis are somewhat obscure and relatively less developed. Community detection in such instances is frequently approached by first projecting the network onto a unipartite network, a method where edges between node classes are encoded as edges within one class. Here we test seven different projection schemes by assessing the performance of community detection on both: (i) a real-world dataset from social media and (ii) an ensemble of artificial networks with prescribed community structure. A number of performance and accuracy issues become apparent from the experimental findings, especially in the case of long-tailed degree distributions. Of the methods tested, the “hyperbolic” projection scheme alleviates most of these difficulties and is thus the most robust scheme of those tested. We conclude that any interpretation of community detection algorithm performance on projected networks must be done with care as certain network configurations require strong community preference for the bipartite structure to be reflected in the unipartite communities. Our results have implications for the analysis of detected community structure in projected unipartite networks.

Keywords

Hocine CherifiStanley Wassermanbipartite networksunpartite projectioncommunity detectionedge weighting
Type
Research Article
Information
Network Science , Volume 8 , Issue S1: Complex Networks 2018 , July 2020 , pp. S145 - S163
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© Cambridge University Press 2020

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