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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

References

Alzahrani, T., & Horadam, K. J. (2014). Analysis of two crime-related networks derived from bipartite social networks. In Proceedings of the 2014 IEEE/ACM international conference on advances in social networks analysis and mining, ASONAM 2014 (pp. 890–897).CrossRefGoogle Scholar
Arthur, R. (2019). Modularity and Projection of Bipartite Networks. arXiv e-prints, arXiv:1908.02520.Google Scholar
Barber, M. J. (2007). Modularity and community detection in bipartite networks. Physical Review E, 76(Dec), 066102.CrossRefGoogle ScholarPubMed
Beckett, S. J. (2016). Improved community detection in weighted bipartite networks. Royal Society Open Science, 3(1).CrossRefGoogle ScholarPubMed
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.CrossRefGoogle Scholar
Bongiorno, C., London, A., Miccichè, S., & Mantegna, R. N. (2017). Core of communities in bipartite networks. Physical Review E, 96(Aug), 022321.CrossRefGoogle ScholarPubMed
Callaway, D. S., Hopcroft, J. E., Kleinberg, J. M., Newman, M. E. J., & Strogatz, S. H. (2001). Are randomly grown graphs really random? Physical Review E, 64(Sep), 041902.CrossRefGoogle ScholarPubMed
Cann, T. J. B., Weaver, I. S., & Williams, H. T. P. (2019). Is it correct to project and detect? Assessing performance of community detection on unipartite projections of bipartite networks. In Complex networks and their applications VII (pp. 267–279). Springer International Publishing.CrossRefGoogle Scholar
Chen, Y.-Z., Li, N., & He, D.-R. (2007). A study on some urban bus transport networks. Physica A: Statistical Mechanics and its Applications, 376, 747–754.CrossRefGoogle Scholar
Del Vicario, M., Zollo, F., Caldarelli, G., Scala, A., & Quattrociocchi, W. (2017). Mapping social dynamics on Facebook: The Brexit debate. Social Networks, 50, 6–16.CrossRefGoogle Scholar
Everett, M. G., & Borgatti, S. P. (2013). The dual-projection approach for two-mode networks. Social Networks, 35(2), 204–210. Special Issue on Advances in Two-mode Social Networks.CrossRefGoogle Scholar
Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019). Fake news on Twitter during the 2016 U.S. presidential election. Science, 363(6425), 374–378.CrossRefGoogle ScholarPubMed
Guimerà, R., Sales-Pardo, M., & Amaral, L. A. N. (2007). Module identification in bipartite and directed networks. Physical Review E, 76(Sep), 036102.CrossRefGoogle ScholarPubMed
Isah, H., Neagu, D., & Trundle, P. (2015). Bipartite network model for inferring hidden ties in crime data. In Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015. ASONAM 2015 (pp. 994–1001).CrossRefGoogle Scholar
Jacomy, M., Venturini, T., Heymann, S., & Bastian, M. (2014). ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLOS ONE, 9(6), 1–12.CrossRefGoogle ScholarPubMed
Larivière, V., Gingras, Y., & Archambault, É. (2009). The decline in the concentration of citations, 1900–2007. Journal of the American Society for Information Science and Technology, 60(4), 858–862.CrossRefGoogle Scholar
Larremore, D. B., Clauset, A., & Jacobs, A. Z. (2014). Efficiently inferring community structure in bipartite networks. Physical Review E, 90(Jul), 012805.CrossRefGoogle ScholarPubMed
Lee, S., Song, S.-i., Kahng, M., Lee, D., & Lee, S.-g. (2011). Random walk based entity ranking on graph for multidimensional recommendation. In Proceedings of the fifth ACM conference on recommender systems (pp. 93–100). ACM.CrossRefGoogle Scholar
Li, Y., & You, C. (2013). What is the difference of research collaboration network under different projections: Topological measurement and analysis. Physica A: Statistical Mechanics and its Applications, 392(15), 3248–3259.CrossRefGoogle Scholar
Marquitti, F. M. D., Guimarães, P. R., Pires, M. M., & Bittencourt, L. F. (2014). MODULAR: software for the autonomous computation of modularity in large network sets. Ecography, 37(3), 221–224.CrossRefGoogle Scholar
Melamed, D. (2014). Community structures in bipartite networks: A dual-projection approach. PLOS ONE, 9(5), 1–5.CrossRefGoogle ScholarPubMed
Newman, M. E. J. (2001). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E, 64, 016132.CrossRefGoogle ScholarPubMed
Newman, M. E. J. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577–8582.CrossRefGoogle ScholarPubMed
Newman, M. E. J. (2005). Power laws, Pareto distributions and Zipf’s law. Contemporary Physics, 46(5), 323–351.CrossRefGoogle Scholar
Romano, S., Vinh, N. X., Bailey, J., & Verspoor, K. (2016). Adjusting for chance clustering comparison measures. Journal of Machine Learning Research, 17(134), 1–32.Google Scholar
Saracco, F., Straka, M. J., Di Clemente, R., Gabrielli, A., Caldarelli, G., & Squartini, T. (2017). Inferring monopartite projections of bipartite networks: an entropy-based approach. New Journal of Physics, 19(5), 053022.CrossRefGoogle Scholar
Sasahara, K. (2016). Visualizing collective attention using association networks. New Generation Computing, 34(4), 323–340.CrossRefGoogle Scholar
Schmidt, A. L., Zollo, F., Del Vicario, M., Bessi, A., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2017). Anatomy of news consumption on Facebook. Proceedings of the National Academy of Sciences, 114(12), 3035–3039.CrossRefGoogle ScholarPubMed
Srivastava, A., Soto, A. J., & Milios, E. (2013). Text clustering using one-mode projection of document-word bipartite graphs. In Proceedings of the 28th annual ACM symposium on applied computing, SAC 2013 (pp. 927–932).CrossRefGoogle Scholar
Starbird, K. (2017). Examining the alternative media ecosystem through the production of alternative narratives of mass shooting events on Twitter. In Proceeding of the international AAAI conference on web and social media.Google Scholar
Wang, Z., Hou, T., Song, D., Li, Z., & Kong, T. (2016). Detecting review spammer groups via bipartite graph projection. The Computer Journal, 59(6), 861–874.CrossRefGoogle Scholar
Weaver, I. S. (2015). Preferential attachment in randomly grown networks. Physica A: Statistical Mechanics and its Applications, 439, 85–92.CrossRefGoogle Scholar
Williams, M., Cioroianu, I., & Williams, H. (2016). Different news for different views: Political news-sharing communities on social media through the UK General Election in 2015. In Proceedings of the workshop on news and public opinion (NECO) at international AAAI conference on web and social media.Google Scholar
Wyse, J., Friel, N., & Latouche, P. (2017) Inferring structure in bipartite networks using the latent blockmodel and exact ICL. Network Science, 5(1), 45–69.CrossRefGoogle Scholar
Yan, E., & Ding, Y. (2012). Scholarly network similarities: How bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other. Journal of the American Society for Information Science and Technology, 63(7), 1313–1326.CrossRefGoogle Scholar
Zhou, C., Feng, L., & Zhao, Q. (2018). A novel community detection method in bipartite networks. Physica A: Statistical Mechanics and its Applications, 492, 1679–1693.CrossRefGoogle Scholar
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