Mutual Community Detection across Multiple Partially Aligned Social Networks

Community detection in online social networks has been a hot research topic in recent years. Meanwhile, to enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously, some of which can share common information and structures. Networks that involve some common users are named as multiple "partially aligned networks". In this paper, we want to detect communities of multiple partially aligned networks simultaneously, which is formally defined as the "Mutual Clustering" problem. The "Mutual Clustering" problem is very challenging as it has two important issues to address: (1) how to preserve the network characteristics in mutual community detection? and (2) how to utilize the information in other aligned networks to refine and disambiguate the community structures of the shared users? To solve these two challenges, a novel community detection method, MCD (Mutual Community Detector), is proposed in this paper. MCD can detect social community structures of users in multiple partially aligned networks at the same time with full considerations of (1) characteristics of each network, and (2) information of the shared users across aligned networks. Extensive experiments conducted on two real-world partially aligned heterogeneous social networks demonstrate that MCD can solve the "Mutual Clustering" problem very well.

[1]  Philip S. Yu,et al.  Transferring heterogeneous links across location-based social networks , 2014, WSDM.

[2]  Philip S. Yu,et al.  PathSim , 2011, Proc. VLDB Endow..

[3]  Hui Xiong,et al.  Understanding of Internal Clustering Validation Measures , 2010, 2010 IEEE International Conference on Data Mining.

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

[5]  Wotao Yin,et al.  A feasible method for optimization with orthogonality constraints , 2013, Math. Program..

[6]  Philip S. Yu,et al.  GConnect: A Connectivity Index for Massive Disk-resident Graphs , 2009, Proc. VLDB Endow..

[7]  Mohammad Al Hasan,et al.  A Survey of Link Prediction in Social Networks , 2011, Social Network Data Analytics.

[8]  Philip S. Yu,et al.  Inferring anchor links across multiple heterogeneous social networks , 2013, CIKM.

[9]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[10]  Curt Jones,et al.  A Heuristic for Reducing Fill-In in Sparse Matrix Factorization , 1993, PPSC.

[11]  Sherali Zeadally,et al.  Multiple Account Identity Deception Detection in Social Media Using Nonverbal Behavior , 2014, IEEE Transactions on Information Forensics and Security.

[12]  Michalis Vazirgiannis,et al.  Clustering and Community Detection in Directed Networks: A Survey , 2013, ArXiv.

[13]  Charu C. Aggarwal,et al.  Community Detection with Edge Content in Social Media Networks , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[14]  Sham M. Kakade,et al.  Multi-view clustering via canonical correlation analysis , 2009, ICML '09.

[15]  Ling Liu,et al.  Social influence based clustering of heterogeneous information networks , 2013, KDD.

[16]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Ana L. N. Fred,et al.  Probabilistic consensus clustering using evidence accumulation , 2013, Machine Learning.

[18]  George Karypis,et al.  Multilevel k-way Partitioning Scheme for Irregular Graphs , 1998, J. Parallel Distributed Comput..

[19]  Philip S. Yu,et al.  Synergistic partitioning in multiple large scale social networks , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[20]  Vipin Kumar,et al.  Parallel Multilevel k-way Partitioning Scheme for Irregular Graphs , 1996, Proceedings of the 1996 ACM/IEEE Conference on Supercomputing.

[21]  Hairong Kuang,et al.  The Hadoop Distributed File System , 2010, 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST).

[22]  Philip S. Yu,et al.  CrossClus: user-guided multi-relational clustering , 2007, Data Mining and Knowledge Discovery.

[23]  Tao Li,et al.  Extending Consensus Clustering to Explore Multiple Clustering Views , 2011, SDM.

[24]  Philip S. Yu,et al.  Integrated Anchor and Social Link Predictions across Social Networks , 2015, IJCAI.

[25]  Philip S. Yu,et al.  Predicting Social Links for New Users across Aligned Heterogeneous Social Networks , 2013, 2013 IEEE 13th International Conference on Data Mining.

[26]  Steffen Bickel,et al.  Multi-view clustering , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[27]  Philip S. Yu,et al.  Community Detection for Emerging Networks , 2015, SDM.

[28]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[29]  Dino Pedreschi,et al.  A classification for community discovery methods in complex networks , 2011, Stat. Anal. Data Min..

[30]  Yizhou Sun,et al.  Ranking-based clustering of heterogeneous information networks with star network schema , 2009, KDD.

[31]  David B. Dunson,et al.  Bayesian consensus clustering , 2013, Bioinform..

[32]  Richard M. Leahy,et al.  An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Feiping Nie,et al.  Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Multi-View K-Means Clustering on Big Data , 2022 .

[34]  Vipin Kumar,et al.  Analysis of Multilevel Graph Partitioning , 1995, Proceedings of the IEEE/ACM SC95 Conference.

[35]  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).

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

[37]  Vladimir Filkov,et al.  Consensus Clustering Algorithms: Comparison and Refinement , 2008, ALENEX.

[38]  Hal Daumé,et al.  A Co-training Approach for Multi-view Spectral Clustering , 2011, ICML.

[39]  Philip S. Yu,et al.  Meta-path based multi-network collective link prediction , 2014, KDD.

[40]  Jiawei Han,et al.  Ranking-based classification of heterogeneous information networks , 2011, KDD.

[41]  Lise Getoor,et al.  Relational clustering for multi-type entity resolution , 2005, MRDM '05.

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

[43]  Philip S. Yu,et al.  Influence Maximization Across Partially Aligned Heterogenous Social Networks , 2015, PAKDD.

[44]  Wei Cheng,et al.  Flexible and robust co-regularized multi-domain graph clustering , 2013, KDD.