Abstract

Link prediction targets to predict the future node interactions mainly based on the current network snapshot. It is a key step in understanding the formation and evolution of the underlying networks; and has practical implications in many real-world applications, ranging from friendship recommendation, click through prediction to targeted advertising. Most existing efforts are devoted to plain networks and assume the availability of network structure in memory before link prediction takes place. However, this assumption is untenable as many real-world networks are affiliated with rich node attributes, and often, the network structure and node attributes are both dynamically evolving at an unprecedented rate. Even though recent studies show that node attributes have an added value to network structure for accurate link prediction, it still remains a daunting task to support link prediction in an online fashion on such dynamic attributed networks. As changes in the dynamic attributed networks are often transient and can be endless, link prediction algorithms need to be efficient by making only one pass of the data with limited memory overhead. To tackle these challenges, we study a novel problem of streaming link prediction on dynamic attributed networks and present a novel framework - SLIDE. Methodologically, SLIDE maintains and updates a low-rank sketching matrix to summarize all observed data, and we further leverage the sketching matrix to infer missing links on the fly. The whole procedure is theoretically guaranteed, and empirical experiments on real-world dynamic attributed networks validate the effectiveness and efficiency of the proposed framework.

Original languageEnglish (US)
Title of host publicationWSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages369-377
Number of pages9
Volume2018-Febuary
ISBN (Electronic)9781450355810
DOIs
StatePublished - Feb 2 2018
Event11th ACM International Conference on Web Search and Data Mining, WSDM 2018 - Marina Del Rey, United States
Duration: Feb 5 2018Feb 9 2018

Other

Other11th ACM International Conference on Web Search and Data Mining, WSDM 2018
CountryUnited States
CityMarina Del Rey
Period2/5/182/9/18

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ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Computer Networks and Communications
  • Information Systems

Cite this

Li, J., Wu, L., Cheng, K., & Liu, H. (2018). Streaming link prediction on dynamic atributed networks. In WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining (Vol. 2018-Febuary, pp. 369-377). Association for Computing Machinery, Inc. https://doi.org/10.1145/3159652.3159674

Streaming link prediction on dynamic atributed networks. / Li, Jundong; Wu, Liang; Cheng, Kewei; Liu, Huan.

WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining. Vol. 2018-Febuary Association for Computing Machinery, Inc, 2018. p. 369-377.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, J, Wu, L, Cheng, K & Liu, H 2018, Streaming link prediction on dynamic atributed networks. in WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining. vol. 2018-Febuary, Association for Computing Machinery, Inc, pp. 369-377, 11th ACM International Conference on Web Search and Data Mining, WSDM 2018, Marina Del Rey, United States, 2/5/18. https://doi.org/10.1145/3159652.3159674
Li J, Wu L, Cheng K, Liu H. Streaming link prediction on dynamic atributed networks. In WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining. Vol. 2018-Febuary. Association for Computing Machinery, Inc. 2018. p. 369-377 https://doi.org/10.1145/3159652.3159674
Li, Jundong ; Wu, Liang ; Cheng, Kewei ; Liu, Huan. / Streaming link prediction on dynamic atributed networks. WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining. Vol. 2018-Febuary Association for Computing Machinery, Inc, 2018. pp. 369-377
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