Multiple social role embedding

Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)

Abstract

Network embedding has been increasingly employed in networked data mining applications as it is effective to learn node embeddings that encode the network structure. Existing network models usually learn a single embedding for each node. In practice, a person may interact with others in different roles, such as interacting with schoolmates as a student, and with colleagues as an employee. Obviously, different roles exhibit different characteristics or features. Hence, only learning a single embedding responsible for all roles is not appropriate. In this paper, we thus introduce a concept of multiple social role (MSR) into social network embedding for the first time. The MSR models multiple roles people play in society, such as student and employee. To make the embedding more versatile, we thus propose a multiple social role embedding (MSRE) model to preserve both the network structure and social roles. Empirical evaluation on various real-world social networks demonstrates advantages of the proposed MSRE over the state-of-the-art embedding models in link prediction and multi-label classification.

Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages581-589
Number of pages9
ISBN (Electronic)9781509050048
DOIs
Publication statusPublished - 2 Jul 2017
Event4th International Conference on Data Science and Advanced Analytics, DSAA 2017 - Tokyo, Japan
Duration: 19 Oct 201721 Oct 2017

Publication series

NameProceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
Volume2018-January

Conference

Conference4th International Conference on Data Science and Advanced Analytics, DSAA 2017
CountryJapan
CityTokyo
Period19/10/1721/10/17

Keywords

  • Data mining
  • Network embedding
  • Social networks

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems and Management
  • Statistics, Probability and Uncertainty
  • Computer Networks and Communications

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