Abstract
This is an extension from a selected paper from JSAI2019. User Identity Linkage (UIL) aims to detect the same individual or entity across different Online Social Networks, which is a crucial step for information diffusion among isolated networks and information transfer between different domains. 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 during process. In this paper, we proposed a novel embedding-based approach that considers and utilizes both individual similarity and community similarity by jointly optimizing them in a single loss function. Experiments conducted on real datasets obtained from Foursquare and Twitter illustrate that the proposed method outperforms other commonly used baselines in UIL, which only consider the individual similarity between users or entities.
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Acknowledgments
This work was funded by JSPS KAKENHI JP16H01836, JP16K12428, and industrial collaborators.
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Wang, Z., Hayashi, T., Ohsawa, Y. (2020). A Community Sensing Approach for User Identity Linkage. In: Ohsawa, Y., et al. Advances in Artificial Intelligence. JSAI 2019. Advances in Intelligent Systems and Computing, vol 1128. Springer, Cham. https://doi.org/10.1007/978-3-030-39878-1_18
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DOI: https://doi.org/10.1007/978-3-030-39878-1_18
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