Host: The Japanese Society for Artificial Intelligence
Name : The 33rd Annual Conference of the Japanese Society for Artificial Intelligence, 2019
Number : 33
Location : [in Japanese]
Date : June 04, 2019 - June 07, 2019
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.