Scalable Social Tie Strength Measuring

Yan Zhong, Xiao Huang, Jundong Li, Xia Hu

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

Abstract

Interpersonal ties describe the intensity of information and activity interactions among individuals. It plays a critical role in social network analysis and sociological studies. Existing efforts focus on leveraging individuals’ non-structural characteristics to measure tie strength. With the booming of online social networks (OSNs), it has become difficult to process and measure all the non-structural data. We study the tie strength measuring from the network topological aspect. However, it remains a nontrivial task due to the controversial comprehensions of its definition and the large volume of OSNs. To tackle the challenges, we develop a scalable measuring framework - IETSM. From the network view, we formally define the tie strength of an edge as the inverse of its impact on the similarity between its two nodes’ influences in information diffusion. To measure this impact, IETSM constructs a node’s influence as the embedding learned from its neighborhoods inductively. It estimates the tie strength of an edge through its impact on its nodes’ influences brought by deleting it. The learned tie strength scores could, in turn, facilitate the node representation learning, and we update them iteratively. Experiments on real-world datasets demonstrate the effectiveness and efficiency of IETSM.
Original languageEnglish
Title of host publication2020 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining
Pages288-295
DOIs
Publication statusPublished - 2020
Event2020 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining -
Duration: 7 Dec 202010 Dec 2020

Publication series

NameIEEE/ACM International Conference on Advances in Social Network Analysis and Mining

Conference

Conference2020 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining
Abbreviated titleASONAM
Period7/12/2010/12/20

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