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Community and Outliers Detection in Social Network

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Big Data Analysis and Deep Learning Applications (ICBDL 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 744))

Abstract

Challenges of detecting communities among users’ interactions play the popular role for days of Social Network. The previous authors proposed for detecting communities in different point of view. However, similarity based on edge structure and nodes which cannot group into communities are still motivating. Considering the community detection is motivating from the similarity measurement to detect significant communities which are high tightly connected each other upon the edge structure and outliers which are unnecessary to group into the communities. This paper is proposed the approach of using similarity measure based on neighborhood overlapping of nodes to organize communities and to identify outliers which cannot be grouped into any of the communities based on Edge Structure. The result implies the best quality with modularity measurement which leads to more accurate communities as well as improved their density after removing outliers in the network structure.

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References

  1. Noble, C.C., Cook, D.J.: Graph-based anomaly detection. In: KDD, 2003, pp. 631–636 (2003)

    Google Scholar 

  2. Hawkins, D.: Identification of Outliers. Chapman and Hall, London (1980)

    Book  Google Scholar 

  3. Jeh, G., Widom, J.: SimRank: a measure of structural-context similarity. In: Proceedings of the 8th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), Edmonton, Alberta, pp. 538–543 (2002)

    Google Scholar 

  4. Moonesinghe, H.D.K., Tan, P.N.: Outrank: a graph-based outlier detection framework using random walk. Int. J. Artif. Intell. Tools 17(1), 19–36 (2008)

    Article  Google Scholar 

  5. Chen, H.-H., Giles, C.L.: ASCOS: an asymmetric network structure context similarity measure. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Niagara Falls, Canada (2013)

    Google Scholar 

  6. Chen, J.Y., Zaiane, O.R., Goebel, R.:. Detecting communities in large networks by iterative local expansion. In: International Conference on Computational Aspects of Social Networks (2009)

    Google Scholar 

  7. Sun, J., Qu, H., Chakrabarti, D., Faloutsos, C.: Neighborhood formation and anomaly detection in bipartite graph. In: ICDM, 27–30 November 2005

    Google Scholar 

  8. Henderson, K., Eliassi-Rad, T., Faloutsos, C., Akoglu, L., Li, L., Maruhashi, K., Prakash, B.A., Tong, H.: Metricforensics: a multi-level approach for mining volatile graphs. In: Proceedings of the 16th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), Washington, DC, pp. 163–172 (2010)

    Google Scholar 

  9. Akoglu, L., McGlohon, M., Faloutsos, C.: Anomaly detection in large graphs. In: CMU-CS-09-173, November 2009

    Google Scholar 

  10. Tang, L., Liu, H.: Community Detection and Mining in Social Media. A Publication in the Morgan & Claypool Publishers series (2010). ISBN 9781608453559

    Article  MathSciNet  Google Scholar 

  11. Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. U.S.A. 99, 7821–7826 (2002)

    Article  MathSciNet  Google Scholar 

  14. Wang, M., Wang, C., Yu, J.X., Zhang, J.: Community detection in social networks: an indepth benchmarking study with a procedure oriented framework. In: 41st International Conference on Very Large Data Bases, Proceedings of the VLDB Endowment, 31 August–4 September 2015, Kohala Coast, Hawaii, vol. 8, no. 10 (2015). Copyright 2015 VLDB Endowment 21508097/15/06

    Article  Google Scholar 

  15. Plantié, M., Crampes, M.: Survey on social community detection. In: Social Media Retrieval, Computer Communications and Networks, pp. 65–85. Springer (2013). ISBN 978-1-4471-4554-7

    Google Scholar 

  16. Chen, M., Nguyen, T., Szymanski, B.K.: On measuring the quality of a network community structure. In: Proceedings of the IEEE Social Computing Conference, Washington DC, 8–14 September, pp. 122–127 (2013)

    Google Scholar 

  17. Zhao, P., Han, J., Sun, Y.: P-rank: a comprehensive structural similarity measure over information networks. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM), Hong Kong, China, pp. 553–562. ACM (2009)

    Google Scholar 

  18. Fortunato, S.: Phys. Rep. 486, 75 (2010)

    Article  MathSciNet  Google Scholar 

  19. Eberle, W., Holder, L.: Discovering structural anomalies in graph-based data. In: ICDM Workshops, pp. 393–398 (2007)

    Google Scholar 

  20. Zachary, W.W.: An information how model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Computer Studies, Mandalay, Mandalay, Myanmar

    Htwe Nu Win

  2. Faculty of Information Science, University of Computer Studies, Mandalay, Mandalay, Myanmar

    Khin Thidar Lynn

Authors
  1. Htwe Nu Win
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  2. Khin Thidar Lynn
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Corresponding author

Correspondence to Htwe Nu Win .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Thi Thi Zin

  2. Department of Computing, Mathematics, and Physics, Western Norway University of Applied Sciences (HVL), Bergen, Norway

    Jerry Chun-Wei Lin

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Win, H.N., Lynn, K.T. (2019). Community and Outliers Detection in Social Network. In: Zin, T., Lin, JW. (eds) Big Data Analysis and Deep Learning Applications. ICBDL 2018. Advances in Intelligent Systems and Computing, vol 744. Springer, Singapore. https://doi.org/10.1007/978-981-13-0869-7_7

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