Relationship Identification Across Heterogeneous Online Social Networks

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

2 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)448-477
Journal / PublicationComputational Intelligence
Volume33
Issue number3
Online published5 Jul 2016
Publication statusPublished - Aug 2017

Abstract

In the era of the social web, many people manage their social relationships through various online social net-working services. It has been found that identifying the types of social relationships among users in online social networks facilitates the marketing of products via electronic “word of mouth.” However, it is a great challenge to identify the types of social relationships, given very limited information in a social network. In this article, we study how to identify the types of relationships across multiple heterogeneous social networks and examine if com-bining certain information from different social networks can help improve the identification accuracy. The main contribution of our research is that we develop a novel decision tree initiated random walk model, which takes into account both global network structure and local user behavior to bootstrap the performance of relationship identification. Experiments conducted based on two real-world social networks, Sina Weibo and Jiepang, demon-strate that the proposed model achieves an average accuracy of 92.0%, significantly outperforming other baseline methods. Our experiments also confirm the effectiveness of combining information from multiple social networks. Moreover, our results reveal that human mobility features indicating location categories, coincidence, and check-in patterns are among the most discriminative features for relationship identification.

Research Area(s)

  • decision tree, heterogeneous social networks, human mobility, random walk, relationship identification

Citation Format(s)

Relationship Identification Across Heterogeneous Online Social Networks. / HE, JIANGNING; LIU, HONGYAN; LAU, RAYMOND Y. K. et al.
In: Computational Intelligence, Vol. 33, No. 3, 08.2017, p. 448-477.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review