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Community Detection

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Broad Learning Through Fusions

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Abstract

In the real-world online social networks, users also tend to form different social groups. Users belonging to the same groups usually have more frequent interactions with each other, while those in different groups will have less interactions on the other hand. Formally, such social groups form by users in online social networks are called the online social communities. Online social communities will partition the network into a number of components, where the intra-community social connections are usually far more dense compared with the inter-community social connections. Meanwhile, from the mathematical representation perspective, due to these online social communities, the social network adjacency matrix tend to be not only sparse but also low-rank.

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References

  1. C. Aggarwal, Y. Xie, P. Yu, Gconnect: a connectivity index for massive disk-resident graphs. Proc. VLDB Endow. 2(1), 862–873 (2009)

    Article  Google Scholar 

  2. A. Arenas, L. Danon, A. Díaz-Guilera, P.M. Gleiser, R. Guimerá, Community analysis in social networks. Eur. Phys. J. B 38(2), 373–380 (2004)

    Article  Google Scholar 

  3. I. Bhattacharya, L. Getoor, Relational clustering for multi-type entity resolution, in Proceedings of the 4th International Workshop on Multi-Relational Mining (ACM, New York, 2005), pp. 3–12

    Google Scholar 

  4. S. Bickel, T. Scheffer, Multi-view clustering, in Fourth IEEE International Conference on Data Mining (ICDM’04), vol 4 (ACM, New York, 2004), pp. 19–26

    Book  Google Scholar 

  5. T. Bui, C. Jones, A heuristic for reducing fill-in in sparse matrix factorization, in Proceedings of the Sixth SIAM Conference on Parallel Processing for Scientific Computing, PPSC 1993, Norfolk, Virginia (1993), pp. 445–452

    Google Scholar 

  6. X. Cai, F. Nie, H. Huang, Multi-view k-means clustering on big data, in Twenty-Third International Joint Conference on Artificial Intelligence (2013), pp. 2598–2604

    Google Scholar 

  7. W. Cheng, X. Zhang, Z. Guo, Y. Wu, P. Sullivan, W. Wang, Flexible and robust co-regularized multi-domain graph clustering, in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2013), pp. 320–328

    Google Scholar 

  8. P. Cimiano, A. Hotho, S. Staab, Comparing conceptual, divisive and agglomerative clustering for learning taxonomies from text, in Proceedings of the 16th European Conference on Artificial Intelligence (ECAI’2004) (2004), pp. 435–439

    Google Scholar 

  9. J. Dean, S. Ghemawat, MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  10. M. Eslami, A. Aleyasen, R. Moghaddam, K. Karahalios, Friend grouping algorithms for online social networks: preference, bias, and implications, in International Conference on Social Informatics (Springer, Cham, 2014), pp. 34–49

    Google Scholar 

  11. P. Gács, L. Lovász, Complexity of algorithms. Lect. Notes (1999)

    Google Scholar 

  12. A. Goder, V. Filkov, Consensus clustering algorithms: comparison and refinement, in Proceedings of the 9th Workshop on Algorithm Engineering and Experiments (Society for Industrial and Applied Mathematics, Philadelphia, 2008), pp. 109–117

    Google Scholar 

  13. M. Hasan, M.J. Zaki, A survey of link prediction in social networks, in Social Network Data Analytics, ed. by C.C. Aggarwal, (Springer, Boston, 2011), pp. 243–275

    Chapter  Google Scholar 

  14. T. Hastie, R. Tibshirani, J. Friedman, Hierarchical clustering, in The Elements of Statistical Learning, 2nd edn., ed. by T. Hastie, R. Tibshirani, J. Friedman (Springer, New York, 2009), pp. 520–528

    Chapter  Google Scholar 

  15. Z. Huang, Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Disc. 2(3), 283–304 (1998)

    Article  Google Scholar 

  16. S. Jin, J. Zhang, P. Yu, S. Yang, A. Li, Synergistic partitioning in multiple large scale social networks, in IEEE BigData (IEEE, Piscataway, 2014), pp. 281–290

    Google Scholar 

  17. G. Karypis, V. Kumar, Analysis of multilevel graph partitioning, in Supercomputing (IEEE, Piscataway, 1995), p. 29

    Google Scholar 

  18. G. Karypis, V. Kumar, Parallel multilevel k-way partitioning scheme for irregular graphs, in Proceedings of the 1996 ACM/IEEE Conference on Supercomputing (Supercomputing ’96) (IEEE, Piscataway, 1996)

    Google Scholar 

  19. G. Karypis, V. Kumar, Multilevel k-way partitioning scheme for irregular graphs. J. Parallel Distrib. Comput. 20(1), 359–392 (1998)

    MATH  Google Scholar 

  20. X. Kong, J. Zhang, P. Yu, Inferring anchor links across multiple heterogeneous social networks, in Proceedings of the 22nd ACM International Conference on Information & Knowledge Management (ACM, New York, 2013), pp. 179–188

    Google Scholar 

  21. A. Kumar, H. Daumé, A co-training approach for multi-view spectral clustering, in Proceedings of the 28th International Conference on Machine Learning (ICML-11) (2011), pp. 393–400

    Google Scholar 

  22. J. Leskovec, K. Lang, M. Mahoney, Empirical comparison of algorithms for network community detection, in Proceedings of the 19th International Conference on World Wide Web (ACM, New York, 2010), pp. 631–640

    Google Scholar 

  23. T. Li, C. Ding, M.I. Jordan, Solving consensus and semi-supervised clustering problems using nonnegative matrix factorization, in Seventh IEEE International Conference on Data Mining (ICDM 2007) (IEEE, Piscataway, 2007), pp. 577–582

    Book  Google Scholar 

  24. D. Liben-Nowell, J. Kleinberg, The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)

    Article  Google Scholar 

  25. W. Lin, X. Kong, P. Yu, Q. Wu, Y. Jia, C. Li, Community detection in incomplete information networks, in Proceedings of the 21st International Conference on World Wide Web (ACM, New York, 2012), pp. 341–350

    Google Scholar 

  26. E.F. Lock, D.B. Dunson, Bayesian consensus clustering. Bioinformatics 29(20), 2610–2616 (2013)

    Article  Google Scholar 

  27. A. Lourenço, S.R. Bulò, N. Rebagliati, A.L.N. Fred, M.A.T. Figueiredo, M. Pelillo, Probabilistic consensus clustering using evidence accumulation. Mach. Learn. 98(1–2), 331–357, (2013)

    MathSciNet  MATH  Google Scholar 

  28. F.D. Malliaros, M. Vazirgiannis, Clustering and community detection in directed networks: a survey. CoRR, abs/1308.0971, abs/1308.0971 (2013)

    Google Scholar 

  29. M. Newman, Modularity and community structure in networks. Proc. Natl. Acad. Sci. U. S. A. 103(23), 8577–8582 (2006)

    Article  Google Scholar 

  30. M.E.J. Newman, M. Girvan, Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)

    Google Scholar 

  31. N. Nguyen, R. Caruana, Consensus clusterings, in Seventh IEEE International Conference on Data Mining (ICDM 2007) (IEEE, Piscataway, 2007), pp. 607–612

    Book  Google Scholar 

  32. S. Pan, Q. Yang, A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)

    Article  Google Scholar 

  33. R. Panigrahy, M. Najork, Y. Xie, How user behavior is related to social affinity, in Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (WSDM ’12) (ACM, New York, 2012), pp. 713–722

    Google Scholar 

  34. P. Petersen, Linear Algebra (Springer, New York, 2012)

    Book  Google Scholar 

  35. M. Richardson, P. Domingos, Mining knowledge-sharing sites for viral marketing, in Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’02) (ACM, New York, 2002), pp. 61–70

    Google Scholar 

  36. R. Roman, Community-based recommendations to improve intranet users’ productivity, Master’s thesis (2016)

    Google Scholar 

  37. W. Shao, J. Zhang, L. He, P. Yu, Multi-source multi-view clustering via discrepancy penalty, in 2016 International Joint Conference on Neural Networks (IJCNN) (IEEE, Piscataway, 2016), pp. 2714–2721

    Book  Google Scholar 

  38. J. Shi, J. Malik, Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)

    Article  Google Scholar 

  39. K. Shvachko, H. Kuang, S. Radia, R. Chansler, The Hadoop distributed file system, in 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST) (IEEE, Piscataway, 2010), pp. 1–10

    Google Scholar 

  40. Y. Sun, Y. Yu, J. Han, Ranking-based clustering of heterogeneous information networks with star network schema, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2009), pp. 797–806

    Google Scholar 

  41. Y. Sun, J. Han, X. Yan, P. Yu, T. Wu, PathSim: meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endow. 4(11), 992–1003 (2011)

    Google Scholar 

  42. Y. Sun, C. Aggarwal, J. Han, Relation strength-aware clustering of heterogeneous information networks with incomplete attributes. Proc. VLDB Endow. 5(5), 394–405 (2012)

    Article  Google Scholar 

  43. J. Tang, H. Gao, X. Hu, H. Liu, Exploiting homophily effect for trust prediction, in Proceedings of the Sixth ACM International Conference on Web Search and Data Mining (ACM, New York, 2013), pp. 53–62

    Google Scholar 

  44. M. Trusov, A. Bodapati, R. Bucklin, Determining influential users in internet social networks. J. Mark. Res. 47(4), 643–658 (2010)

    Article  Google Scholar 

  45. U. von Luxburg, A tutorial on spectral clustering. Stat. Comput. 17(4) (2007). arXiv:0711.0189

    Google Scholar 

  46. X. Wang, G. Chen, Complex networks: small-world, scale-free and beyond. IEEE Circuits Syst. Mag. 3(1), 6–20 (2003)

    Article  Google Scholar 

  47. S. Wang, Z. Zhang, J. Li, A scalable cur matrix decomposition algorithm: lower time complexity and tighter bound. Mach. Learn. (2012). arXiv:1210.1461

    Google Scholar 

  48. Z. Wen, W. Yin, A feasible method for optimization with orthogonality constraints, Technical report, Rice University (2010)

    MATH  Google Scholar 

  49. X. Yin, J. Han, P. Yu, CrossClus: user-guided multi-relational clustering. Data Min. Knowl. Disc. 15(3), 321–348 (2007)

    Article  MathSciNet  Google Scholar 

  50. Q. Zhan, J. Zhang, S. Wang, P. Yu, J. Xie, Influence maximization across partially aligned heterogeneous social networks, in Pacific-Asia Conference on Knowledge Discovery and Data Mining (Springer, Cham, 2015), pp. 58–69

    Google Scholar 

  51. J. Zhang, P. Yu, Integrated anchor and social link predictions across partially aligned social networks, in Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)

    Google Scholar 

  52. J. Zhang, P. Yu, Mcd: mutual clustering across multiple social networks, in 2015 IEEE International Congress on Big Data (IEEE, Piscataway, 2015). http://10.1109/BigDataCongress.2015.127

    Google Scholar 

  53. J. Zhang, P. Yu, Community detection for emerging networks, in Proceedings of the 2015 SIAM International Conference on Data Mining (Society for Industrial and Applied Mathematics, Philadelphia, 2015), pp. 127–135

    Book  Google Scholar 

  54. J. Zhang, X. Kong, P. Yu, Predicting social links for new users across aligned heterogeneous social networks, in 2013 IEEE 13th International Conference on Data Mining (IEEE, Piscataway, 2013), pp. 1289–1294

    Google Scholar 

  55. J. Zhang, X. Kong, P. Yu, Transferring heterogeneous links across location-based social networks, in Proceedings of the 7th ACM International Conference on Web Search and Data Mining (ACM, New York, 2014), pp. 303–312

    Google Scholar 

  56. J. Zhang, P. Yu, Z. Zhou, Meta-path based multi-network collective link prediction, in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2014), pp. 1286–1295

    Google Scholar 

  57. J. Zhang, P. Yu, Y. Lv, Q. Zhan, Information diffusion at workplace, in Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (ACM, Piscataway, 2016), pp. 1673–1682

    Google Scholar 

  58. J. Zhang, J. Chen, S. Zhi, Y. Chang, P. Yu, J. Han, Link prediction across aligned networks with sparse low rank matrix estimation, in 2017 IEEE 33rd International Conference on Data Engineering (ICDE) (IEEE, Piscataway, 2017), pp. 971–982

    Google Scholar 

  59. J. Zhang, L. Cui, P. Yu, Y. Lv, BL-ECD: broad learning based enterprise community detection via hierarchical structure fusion, in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (ACM, New York, 2017), pp. 859–868

    Google Scholar 

  60. J. Zhang, P. Yu, Y. Lv, Enterprise community detection, in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (ACM, New York, 2017), pp. 859–868

    Google Scholar 

  61. Y. Zhao, E. Levina, J. Zhu, Community extraction for social networks. Proc. Natl. Acad. Sci. USA 108(18), 7321–7326 (2011)

    Article  Google Scholar 

  62. Y. Zhou, L. Liu, Social influence based clustering of heterogeneous information networks, in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York, 2013), pp. 338–346

    Google Scholar 

  63. Y. Zhou, H. Cheng, J. Yu, Graph clustering based on structural/attribute similarities. Proc. VLDB Endow. 2(1), 718–729 (2009)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Florida State University, Tallahassee, FL, USA

    Jiawei Zhang

  2. Department of Computer Science, University of Illinois, Chicago, IL, USA

    Philip S. Yu

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  1. Jiawei Zhang
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  2. Philip S. Yu
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Zhang, J., Yu, P.S. (2019). Community Detection. In: Broad Learning Through Fusions. Springer, Cham. https://doi.org/10.1007/978-3-030-12528-8_8

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  • DOI: https://doi.org/10.1007/978-3-030-12528-8_8

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