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dc.contributorDepartment of Computingen_US
dc.creatorZhong, Yen_US
dc.creatorZhang, Cen_US
dc.creatorHuangy, Xen_US
dc.creatorLiz, Jen_US
dc.creatorHu, Xen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.rightsThe following publication Y. Zhong, X. Huang, J. Li and X. Hu, "Scalable Social Tie Strength Measuring," 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2020, pp. 288-295 is available at
dc.subjectTie strengthen_US
dc.subjectOnline social networksen_US
dc.subjectInductive embeddingen_US
dc.titleScalable social tie strength measuringen_US
dc.typeConference Paperen_US
dcterms.abstractInterpersonal 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.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitation2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 7-10 December 2020, p. 288-295en_US
dc.relation.conferenceIEEE/ACM International Conference on Advances in Social Networks Analysis and Mining [ASONAM]en_US
dc.description.validate202103 bcrcen_US
dc.description.oaAccepted Manuscripten_US
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