Learning community embedding with community detection and node embedding on graphs

Sandro Cavallari, Vincent W. Zheng, Hongyun Cai, Kevin Chen Chuan Chang, Erik Cambria

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

Abstract

In this paper, we study an important yet largely under-explored setting of graph embedding, i.e., embedding communities instead of each individual nodes. We find that community embedding is not only useful for community-level applications such as graph visualization, but also beneficial to both community detection and node classification. To learn such embedding, our insight hinges upon a closed loop among community embedding, community detection and node embedding. On the one hand, node embedding can help improve community detection, which outputs good communities for fitting better community embedding. On the other hand, community embedding can be used to optimize the node embedding by introducing a community-aware high-order proximity. Guided by this insight, we propose a novel community embedding framework that jointly solves the three tasks together. We evaluate such a framework on multiple real-world datasets, and show that it improves graph visualization and outperforms state-of-the-art baselines in various application tasks, e.g., community detection and node classification.

Original languageEnglish (US)
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages377-386
Number of pages10
ISBN (Electronic)9781450349185
DOIs
StatePublished - Nov 6 2017
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore
Duration: Nov 6 2017Nov 10 2017

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
VolumePart F131841

Other

Other26th ACM International Conference on Information and Knowledge Management, CIKM 2017
CountrySingapore
CitySingapore
Period11/6/1711/10/17

Keywords

  • Community embedding
  • Graph embedding

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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  • Cite this

    Cavallari, S., Zheng, V. W., Cai, H., Chang, K. C. C., & Cambria, E. (2017). Learning community embedding with community detection and node embedding on graphs. In CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management (pp. 377-386). (International Conference on Information and Knowledge Management, Proceedings; Vol. Part F131841). Association for Computing Machinery. https://doi.org/10.1145/3132847.3132925