Proceedings of the Annual Conference of JSAI
Online ISSN : 2758-7347
33rd (2019)
Session ID : 3B4-E-2-02
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A Community Sensing Approach for User Identity Linkage
*Zexuan WANGTeruaki HAYASHIYukio OHSAWA
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CONFERENCE PROCEEDINGS FREE ACCESS

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Abstract

User Identity Linkage aims to detect the same individual or entity across different Online Social Networks, which is a crucial step for information diffusion among isolated networks. While many pair-wise user linking methods have been proposed on this important topic, the community information naturally exists in the network is often discarded. In this paper, we proposed a novel embedding-based approach that considers both individual similarity and community similarity by jointly optimize them in a single loss function. Experiments on real dataset obtained from Foursquare and Twitter illustrate that proposed method outperforms other commonly used baselines that only consider the individual similarity.

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© 2019 The Japanese Society for Artificial Intelligence
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