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Community Detection with Topological Structure and Attributes in Information Networks

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Published:02 November 2016Publication History
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Abstract

Information networks contain objects connected by multiple links and described by rich attributes. Detecting community for these networks is a challenging research problem, because there is a scarcity of effective approaches that balance the features of the network structure and the characteristics of the nodes. Some methods detect communities by considering topological structures while ignoring the contributions of attributes. Other methods have considered both topological structure and attributes but pay a high price in time complexity. We establish a new community detection algorithm which explores both topological <u>S</u>tructure and <u>A</u>ttributes using <u>G</u>lobal structure and <u>L</u>ocal neighborhood features (SAGL) which also has low computational complexity. The first step of SAGL evaluates the global importance of every node and calculates the similarity of each node pair by combining edge strength and node attribute similarity. The second step of SAGL uses a clustering algorithm that identifies communities by measuring the similarity of two nodes, calculated by the distance of their neighbors. Experimental results on three real-world datasets show the effectiveness of SAGL, particularly its fast convergence compared to current state-of-the-art attributed graph clustering methods.

References

  1. Lada A. Adamic and Natalie Glance. 2005. The political blogosphere and the 2004 US election: Divided they blog. In Proceedings of the 3rd International Workshop on Link Discovery. ACM, 36--43. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Charu C. Aggarwal, Yan Xie, and S. Yu Philip. 2011. Towards community detection in locally heterogeneous networks. In SDM. SIAM, 391--402.Google ScholarGoogle Scholar
  3. Arvind Arasu, Junghoo Cho, Hector Garcia-Molina, Andreas Paepcke, and Sriram Raghavan. 2001. Searching the web. ACM Trans. Internet Technol. 1, 1 (2001), 2--43. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3 (2003), 993--1022. Google ScholarGoogle ScholarCross RefCross Ref
  5. Jie Chen and Yousef Saad. 2012. Dense subgraph extraction with application to community detection. IEEE Trans. Knowl. Data Eng. 24, 7 (2012), 1216--1230. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Yi-Cheng Chen, Wen-Yuan Zhu, Wen-Chih Peng, Wang-Chien Lee, and Suh-Yin Lee. 2014. CIM: Community-based influence maximization in social networks. ACM Trans. Intell. Syst. Technol. 5, 2 (2014), 25. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Hong Cheng, Yang Zhou, and Jeffrey Xu Yu. 2011. Clustering large attributed graphs: A balance between structural and attribute similarities. ACM Trans. Knowl. Discov. Data 5, 2 (2011), 12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Juan David Cruz, Cécile Bothorel, and François Poulet. 2013. Community detection and visualization in social networks: Integrating structural and semantic information. ACM Trans. Intell. Syst. Technol. 5, 1 (2013), 11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. T. A. Dang and E. Viennet. 2012. Community detection based on structural and attribute similarities. In Proceedings of the International Conference on Digital Society (ICDS). 7--12.Google ScholarGoogle Scholar
  10. Pasquale De Meo, Emilio Ferrara, Giacomo Fiumara, and Alessandro Provetti. 2014. Mixing local and global information for community detection in large networks. J. Comput. Syst. Sci. 80, 1 (2014), 72--87. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Himel Dev. 2014. A user interaction based community detection algorithm for online social networks. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. ACM, 1607--1608. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Ying Ding, Erjia Yan, Arthur Frazho, and James Caverlee. 2009. PageRank for ranking authors in co-citation networks. J. Am. Soc. Inform. Sci. Technol. 60, 11 (2009), 2229--2243. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Michelle Girvan and Mark E. J. Newman. 2002. Community structure in social and biological networks. Proc. Natl. Acad. Sci. U.S.A. 99, 12 (2002), 7821--7826.Google ScholarGoogle ScholarCross RefCross Ref
  14. Leonard Kaufman and Peter Rousseeuw. 1987. Clustering by Means of Medoids. North-Holland.Google ScholarGoogle Scholar
  15. M. E. J. Newman. 2013. Community detection and graph partitioning. Europhys. Lett. 103, 2 (2013), 28003.Google ScholarGoogle ScholarCross RefCross Ref
  16. Mark E. J. Newman. 2006. Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, 3 (2006), 036104.Google ScholarGoogle ScholarCross RefCross Ref
  17. Athanasios Papadopoulos, George Pallis, and Marios D. Dikaiakos. 2013. Identifying clusters with attribute homogeneity and similar connectivity in information networks. In Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), Vol. 1. IEEE, 343--350. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Sumeet Singh and Amit Awekar. 2013. Incremental shared nearest neighbor density-based clustering. In Proceedings of the 22nd ACM International Conference on Conference on Information 8 Knowledge Management. ACM, 1533--1536. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Xuning Tang and Christopher C. Yang. 2014. Detecting social media hidden communities using dynamic stochastic blockmodel with temporal Dirichlet process. ACM Trans. Intell. Syst. Technol. 5, 2 (2014), 36. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Yuanyuan Tian, Richard A. Hankins, and Jignesh M. Patel. 2008. Efficient aggregation for graph summarization. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. ACM, 567--580. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Wei Weng, Shunzhi Zhu, and Huarong Xu. 2014. Hierarchical community detection algorithm based on local similarity. J. Dig. Inform. Manag. 12, 4 (2014), 275.Google ScholarGoogle Scholar
  22. Zhiqiang Xu, Yiping Ke, Yi Wang, Hong Cheng, and James Cheng. 2012. A model-based approach to attributed graph clustering. In Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. ACM, 505--516. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Erjia Yan and Ying Ding. 2011. Discovering author impact: A PageRank perspective. Inform. Process. Manag. 47, 1 (2011), 125--134. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Jaewon Yang and Jure Leskovec. 2014. Structure and overlaps of ground-truth communities in networks. ACM Trans. Intell. Syst. Technol. 5, 2 (2014), 26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Jaewon Yang, Julian McAuley, and Jure Leskovec. 2013. Community detection in networks with node attributes. In Proceedings of the 2013 IEEE 13th International Conference on Data Mining (ICDM). IEEE, 1151--1156.Google ScholarGoogle ScholarCross RefCross Ref
  26. Xin Yu, Jing Yang, and Zhi-Qiang Xie. 2015. A semantic overlapping community detection algorithm based on field sampling. Expert Syst. Appl. 42, 1 (2015), 366--375. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Tao Zhou, Linyuan Lü, and Yi-Cheng Zhang. 2009. Predicting missing links via local information. Eur. Phys. J. B 71, 4 (2009), 623--630.Google ScholarGoogle ScholarCross RefCross Ref
  28. Yang Zhou, Hong Cheng, and Jeffrey Xu Yu. 2009. Graph clustering based on structural/attribute similarities. Proc. VLDB Endow. 2, 1 (2009), 718--729. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Yang Zhou, Hong Cheng, and Jeffrey Xu Yu. 2010. Clustering large attributed graphs: An efficient incremental approach. In Proceedings of the 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 689--698. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Transactions on Intelligent Systems and Technology
        ACM Transactions on Intelligent Systems and Technology  Volume 8, Issue 2
        Survey Paper, Special Issue: Intelligent Music Systems and Applications and Regular Papers
        March 2017
        407 pages
        ISSN:2157-6904
        EISSN:2157-6912
        DOI:10.1145/3004291
        • Editor:
        • Yu Zheng
        Issue’s Table of Contents

        Copyright © 2016 ACM

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        Publication History

        • Published: 2 November 2016
        • Accepted: 1 July 2016
        • Revised: 1 February 2016
        • Received: 1 September 2015
        Published in tist Volume 8, Issue 2

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