Abstract

Multiple networks naturally appear in numerous high-impact applications. Network alignment (i.e., finding the node correspondence across different networks) is often the very first step for many data mining tasks. Most, if not all, of the existing alignment methods are solely based on the topology of the underlying networks. Nonetheless, many real networks often have rich attribute information on nodes and/or edges. In this paper, we propose a family of algorithms (FINAL) to align attributed networks. The key idea is to leverage the node/edge attribute information to guide (topology-based) alignment process. We formulate this problem from an optimization perspective based on the alignment consistency principle, and develop effective and scalable algorithms to solve it. Our experiments on real networks show that (1) by leveraging the attribute information, our algo- rithms can significantly improve the alignment accuracy (i.e., up to a 30% improvement over the existing methods); (2) compared with the exact solution, our proposed fast alignment algorithm leads to a more than 10 speed-up, while preserving a 95% ac- curacy; and (3) our on-query alignment method scales linearly, with an around 90% ranking accuracy compared with our exact full alignment method and a near real-time response time.

Original languageEnglish (US)
Title of host publicationKDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages1345-1354
Number of pages10
Volume13-17-August-2016
ISBN (Electronic)9781450342322
DOIs
StatePublished - Aug 13 2016
Event22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016 - San Francisco, United States
Duration: Aug 13 2016Aug 17 2016

Other

Other22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016
CountryUnited States
CitySan Francisco
Period8/13/168/17/16

Fingerprint

Topology
Data mining
Experiments

Keywords

  • Alignment consistency
  • Attributed network alignment
  • Onquery alignment

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Zhang, S., & Tong, H. (2016). FINAL: Fast attributed network alignment. In KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol. 13-17-August-2016, pp. 1345-1354). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939766

FINAL : Fast attributed network alignment. / Zhang, Si; Tong, Hanghang.

KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 13-17-August-2016 Association for Computing Machinery, 2016. p. 1345-1354.

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

Zhang, S & Tong, H 2016, FINAL: Fast attributed network alignment. in KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. vol. 13-17-August-2016, Association for Computing Machinery, pp. 1345-1354, 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2016, San Francisco, United States, 8/13/16. https://doi.org/10.1145/2939672.2939766
Zhang S, Tong H. FINAL: Fast attributed network alignment. In KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 13-17-August-2016. Association for Computing Machinery. 2016. p. 1345-1354 https://doi.org/10.1145/2939672.2939766
Zhang, Si ; Tong, Hanghang. / FINAL : Fast attributed network alignment. KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. 13-17-August-2016 Association for Computing Machinery, 2016. pp. 1345-1354
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