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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
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)
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)
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_36
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_44
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)
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)
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)
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)
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)
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)
Acknowledgements
This work is partially supported by the Key Research and Development Plan of National Ministry of Science and Technology under grant No. 2016YFB1000703.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Wang, X., Gao, H., Wang, J., Yue, T., Li, J. (2018). Detecting Top-k Active Inter-Community Jumpers in Dynamic Information Networks. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-91452-7_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91451-0
Online ISBN: 978-3-319-91452-7
eBook Packages: Computer ScienceComputer Science (R0)