TY - GEN
T1 - Aligning users across social networks using network embedding
AU - Liu, Li
AU - CHEUNG, Kwok Wai
AU - Li, Xin
AU - Liao, Lejian
N1 - Funding Information:
This work has been partially supported by NSFC under Grant No. 61300178, National Program on Key Basic Research Project under Grant No. 2013CB329605, and Hong Kong Baptist University Strategic Development Fund
PY - 2016/7
Y1 - 2016/7
N2 - In this paper, we adopt the representation learning approach to align users across multiple social networks where the social structures of the users are exploited. In particular, we propose to learn a network embedding with the followership/followee-ship of each user explicitly modeled as input/output context vector representations so as to preserve the proximity of users with "similar" followers/followees in the embedded space. For the alignment, we add both known and potential anchor users across the networks to facilitate the transfer of context information across networks. We solve both the network embedding problem and the user alignment problem simultaneously under a unified optimization framework. The stochastic gradient descent and negative sampling algorithms are used to address scalability issues. Extensive experiments on real social network datasets demonstrate the effectiveness and efficiency of the proposed approach compared with several state-of-the-art methods.
AB - In this paper, we adopt the representation learning approach to align users across multiple social networks where the social structures of the users are exploited. In particular, we propose to learn a network embedding with the followership/followee-ship of each user explicitly modeled as input/output context vector representations so as to preserve the proximity of users with "similar" followers/followees in the embedded space. For the alignment, we add both known and potential anchor users across the networks to facilitate the transfer of context information across networks. We solve both the network embedding problem and the user alignment problem simultaneously under a unified optimization framework. The stochastic gradient descent and negative sampling algorithms are used to address scalability issues. Extensive experiments on real social network datasets demonstrate the effectiveness and efficiency of the proposed approach compared with several state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=85006157196&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85006157196
SN - 9781577357704
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 1774
EP - 1780
BT - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16)
PB - AAAI press
T2 - 25th International Joint Conference on Artificial Intelligence, IJCAI 2016
Y2 - 9 July 2016 through 15 July 2016
ER -