Advertisement

Detecting Top-k Active Inter-Community Jumpers in Dynamic Information Networks

  • Xinrui Wang
  • Hong Gao
  • Jinbao Wang
  • Tianbai Yue
  • Jianzhong Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)

Abstract

Dynamic information networks, containing evolving objects and links, exist in various applications. Mining such networks is more challenging than mining static ones. In this paper, we propose a novel concept of Active Inter-Community Jumpers (AICJumpers) for dynamic information networks, which are objects changing communities frequently over time. Given communities of several snapshots in a dynamic network, we devise a time-efficiency top-k AICJumpers detection algorithm with a sliding window model. After denoting the jump score which captures how frequently an object changes communities over time, we encode the community changing trajectory of each object as bit vectors and transform jump scores computation into bitwise and, or and xor operations between bit vectors. We further propose a slide-based strategy for space and time saving. Experiments on both real and synthetic datasets show high effectiveness and efficiency of our methods as well as the significance of the AICJumper concept.

Keywords

Active inter-community jumpers detection Dynamic information networks A sliding window model Bit vectors 

Notes

Acknowledgements

This work is partially supported by the Key Research and Development Plan of National Ministry of Science and Technology under grant No. 2016YFB1000703.

References

  1. 1.
    Bu, Y., Chen, L., Fu, A.W.C., Liu, D.: Efficient anomaly monitoring over moving object trajectory streams. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 159–168. ACM (2009)Google Scholar
  2. 2.
    Gao, J., Liang, F., Fan, W., Wang, C., Sun, Y., Han, J.: On community outliers and their efficient detection in information networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 813–822. ACM (2010)Google Scholar
  3. 3.
    Ge, Y., Xiong, H., Zhou, Z.H., Ozdemir, H., Yu, J., Lee, K.C.: TOP-EYE: top-k evolving trajectory outlier detection. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1733–1736. ACM (2010)Google Scholar
  4. 4.
    Gupta, M., Gao, J., Han, J.: Community distribution outlier detection in heterogeneous information networks. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013. LNCS (LNAI), vol. 8188, pp. 557–573. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40988-2_36CrossRefGoogle Scholar
  5. 5.
    Gupta, M., Gao, J., Sun, Y., Han, J.: Community trend outlier detection using soft temporal pattern mining. In: Flach, P.A., De Bie, T., Cristianini, N. (eds.) ECML PKDD 2012. LNCS (LNAI), vol. 7524, pp. 692–708. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-33486-3_44CrossRefGoogle Scholar
  6. 6.
    Gupta, M., Gao, J., Sun, Y., Han, J.: Integrating community matching and outlier detection for mining evolutionary community outliers. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 859–867. ACM (2012)Google Scholar
  7. 7.
    Lee, J.G., Han, J., Li, X.: Trajectory outlier detection: a partition-and-detect framework. In: IEEE 24th International Conference on Data Engineering, 2008, ICDE 2008, pp. 140–149. IEEE (2008)Google Scholar
  8. 8.
    Sun, Y., Han, J., Aggarwal, C.C., Chawla, N.V.: When will it happen?: relationship prediction in heterogeneous information networks. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 663–672. ACM (2012)Google Scholar
  9. 9.
    Sun, Y., Tang, J., Han, J., Gupta, M., Zhao, B.: Community evolution detection in dynamic heterogeneous information networks. In: Proceedings of the Eighth Workshop on Mining and Learning with Graphs, pp. 137–146. ACM (2010)Google Scholar
  10. 10.
    Sun, Y., Yu, Y., Han, J.: 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, pp. 797–806. ACM (2009)Google Scholar
  11. 11.
    Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.: SCAN: a structural clustering algorithm for networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 824–833. ACM (2007)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Xinrui Wang
    • 1
  • Hong Gao
    • 1
  • Jinbao Wang
    • 1
  • Tianbai Yue
    • 2
  • Jianzhong Li
    • 1
  1. 1.Harbin Institute of TechnologyHarbinChina
  2. 2.Harbin Institute of PetroleumHarbinChina

Personalised recommendations