Abstract
The clustering ensemble paradigm has emerged as an effective tool for community detection in multilayer networks, which allows for producing consensus solutions that are designed to be more robust to the algorithmic selection and configuration bias. However, one limitation is related to the dependency on a co-association threshold that controls the degree of consensus in the community structure solution. The goal of this work is to overcome this limitation with a new framework of ensemble-based multilayer community detection, which features parameter-free identification of consensus communities based on generative models of graph pruning that are able to filter out noisy co-associations. We also present an enhanced version of the modularity-driven ensemble-based multilayer community detection method, in which community memberships of nodes are reconsidered to optimize the multilayer modularity of the consensus solution. Experimental evidence on real-world networks confirms the beneficial effect of using model-based filtering methods and also shows the superiority of the proposed method on state-of-the-art multilayer community detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dianati, N.: Unwinding the hairball graph: pruning algorithms for weighted complex networks. Phys. Rev. E 93, 012304 (2016)
Dickison, M.E., Magnani, M., Rossi, L.: Multilayer Social Networks. Cambridge University Press, UK (2016)
Domenico, M.D., Lancichinetti, A., Arenas, A., Rosvall, M.: Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Phys. Rev. X 5, 011027 (2015)
Domenico, M.D., Nicosia, V., Arenas, A., Latora, V.: Structural reducibility of multilayer networks. Nature Commun. 6, 6864 (2015)
Gemmetto, V., Cardillo, A., Garlaschelli, D.: Irreducible network backbones: unbiased graph filtering via maximum entropy. arXiv (June 2017)
Gullo, F., Tagarelli, A., Greco, S.: Diversity-based weighting schemes for clustering ensembles. In: Proceedings of SIAM Data Mining, pp. 437–448 (2009)
Kim, J., Lee, J.: Community detection in multi-layer graphs: a survey. SIGMOD Rec. 44(3), 37–48 (2015)
Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J.P., Moreno, Y., Porter, M.A.: Multilayer networks. J. Complex Netw. 2(3), 203–271 (2014)
Lancichinetti, A., Fortunato, S.: Consensus clustering in complex networks. Sci. Rep. 2, 336 (2012)
LaSalle, D., Karypis, G.: Multi-threaded modularity based graph clustering using the multilevel paradigm. J. Parallel Distrib. Comput. 76, 66–80 (2015)
Mastrandrea, R., Squartini, T., Fagiolo, G., Garlaschelli, D.: Enhanced reconstruction of weighted networks from strengths and degrees. New J. Phys. 16, 043022 (2014)
Mucha, P.J., Richardson, T., Macon, K., Porter, M.A., Onnela, J.P.: Community structure in time-dependent, multiscale, and multiplex networks. Science 328(5980), 876–878 (2010)
Radicchi, F., Ramasco, J.J., Fortunato, S.: Information filtering in complex weighted networks. Phys. Rev. E 83, 046101 (2011)
Serrano, M.A., Boguna, M., Vespignani, A.: Extracting the multiscale backbone of complex weighted networks. PNAS 106(16), 6483–6488 (2009)
Strehl, A., Ghosh, J.: Cluster ensembles – a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2003)
Tagarelli, A., Amelio, A., Gullo, F.: Ensemble-based community detection in multilayer networks. Data Min. Knowl. Discov. 31(5), 1506–1543 (2017)
Tang, L., Wang, X., Liu, H.: Uncovering groups via heterogeneous interaction analysis. In: Proceedings of IEEE ICDM, pp. 503–512 (2009)
Tang, L., Wang, X., Liu, H.: Community detection via heterogeneous interaction analysis. Data Min. Knowl. Discov. 25, 1–33 (2012)
Zhang, H., Wang, C., Lai, J., Yu, P.S.: Modularity in complex multilayer networks with multiple aspects: a static perspective. CoRR abs/1605.06190 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Mandaglio, D., Amelio, A., Tagarelli, A. (2018). Consensus Community Detection in Multilayer Networks Using Parameter-Free Graph Pruning. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-93040-4_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93039-8
Online ISBN: 978-3-319-93040-4
eBook Packages: Computer ScienceComputer Science (R0)