Ensemble-based community detection in multilayer networks

The problem of community detection in a multilayer network can effectively be addressed by aggregating the community structures separately generated for each network layer, in order to infer a consensus solution for the input network. To this purpose, clustering ensemble methods developed in the data clustering field are naturally of great support. Bringing these methods into a community detection framework would in principle represent a powerful and versatile approach to reach more stable and reliable community structures. Surprisingly, research on consensus community detection is still in its infancy. In this paper, we propose a novel modularity-driven ensemble-based approach to multilayer community detection. A key aspect is that it finds consensus community structures that not only capture prototypical community memberships of nodes, but also preserve the multilayer topology information and optimize the edge connectivity in the consensus via modularity analysis. Empirical evidence obtained on seven real-world multilayer networks sheds light on the effectiveness and efficiency of our proposed modularity-driven ensemble-based approach, which has shown to outperform state-of-the-art multilayer methods in terms of modularity, silhouette of community memberships, and redundancy assessment criteria, and also in terms of execution times.

[1]  S. Fortunato,et al.  Resolution limit in community detection , 2006, Proceedings of the National Academy of Sciences.

[2]  Chris H. Q. Ding,et al.  Solving Consensus and Semi-supervised Clustering Problems Using Nonnegative Matrix Factorization , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[3]  Hareton K. N. Leung,et al.  Incremental Semi-Supervised Clustering Ensemble for High Dimensional Data Clustering , 2016, IEEE Transactions on Knowledge and Data Engineering.

[4]  George Karypis,et al.  Multi-threaded modularity based graph clustering using the multilevel paradigm , 2015, J. Parallel Distributed Comput..

[5]  Jae-Gil Lee,et al.  Community Detection in Multi-Layer Graphs: A Survey , 2015, SGMD.

[6]  F. Radicchi,et al.  Benchmark graphs for testing community detection algorithms. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  Joshua Zhexue Huang,et al.  Incremental density-based ensemble clustering over evolving data streams , 2016, Neurocomputing.

[8]  Huan Liu,et al.  Community detection via heterogeneous interaction analysis , 2012, Data Mining and Knowledge Discovery.

[9]  Giovanni Montana,et al.  Community detection in multiplex networks using Locally Adaptive Random walks , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[10]  Jukka-Pekka Onnela,et al.  Community Structure in Time-Dependent, Multiscale, and Multiplex Networks , 2009, Science.

[11]  Fosca Giannotti,et al.  Finding and Characterizing Communities in Multidimensional Networks , 2011, 2011 International Conference on Advances in Social Networks Analysis and Mining.

[12]  Francesco Calabrese,et al.  ABACUS: frequent pAttern mining-BAsed Community discovery in mUltidimensional networkS , 2013, Data Mining and Knowledge Discovery.

[13]  Michael J. Cafarella,et al.  Link-Prediction Enhanced Consensus Clustering for Complex Networks , 2015, PloS one.

[14]  Rich Caruana,et al.  Consensus Clusterings , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[15]  Clara Pizzuti,et al.  A Cooperative Evolutionary Approach to Learn Communities in Multilayer Networks , 2014, PPSN.

[16]  Huan Liu,et al.  Uncoverning Groups via Heterogeneous Interaction Analysis , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[17]  Mason A. Porter,et al.  Multilayer networks , 2013, J. Complex Networks.

[18]  Inderjit S. Dhillon,et al.  Kernel k-means: spectral clustering and normalized cuts , 2004, KDD.

[19]  Matteo Magnani,et al.  Multilayer Social Networks , 2016 .

[20]  Ana L. N. Fred,et al.  Finding Consistent Clusters in Data Partitions , 2001, Multiple Classifier Systems.

[21]  Rushed Kanawati,et al.  Community detection in multiplex networks: A seed-centric approach , 2015, Networks Heterog. Media.

[22]  Mihaela van der Schaar,et al.  Ensemble Online Clustering through Decentralized Observations , 2014, 53rd IEEE Conference on Decision and Control.

[23]  Ali Pinar,et al.  On Clustering on Graphs with Multiple Edge Types , 2011, Internet Math..

[24]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[25]  Clara Pizzuti,et al.  Community Detection in Multidimensional Networks , 2014, 2014 IEEE 26th International Conference on Tools with Artificial Intelligence.

[26]  Pascal Frossard,et al.  Clustering With Multi-Layer Graphs: A Spectral Perspective , 2011, IEEE Transactions on Signal Processing.

[27]  Joydeep Ghosh,et al.  Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..

[28]  Sergio Greco,et al.  Diversity-Based Weighting Schemes for Clustering Ensembles , 2009, SDM.

[29]  Martin Rosvall,et al.  Maps of random walks on complex networks reveal community structure , 2007, Proceedings of the National Academy of Sciences.

[30]  Philip S. Yu,et al.  Modularity in complex multilayer networks with multiple aspects: a static perspective , 2016, Applied Informatics.

[31]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[32]  Santo Fortunato,et al.  Consensus clustering in complex networks , 2012, Scientific Reports.