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MC2: Unsupervised Multiple Social Network Alignment
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DOI：10.1145/3596514.png)
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Social network alignment, identifying social accounts of the same individual across different social networks, shows fundamental importance in a wide spectrum of applications, such as link prediction and information diffusion. Individuals more often than not join in multiple social networks, and it is in fact much too expensive or even impossible to acquiring supervision for guiding the alignment. To the best of our knowledge, few method in the literature can align multiple social networks without supervision. In this article, we propose to study the problem of unsupervised multiple social network alignment. To address this problem, we propose a novel unsupervised model of joint Matrix factorization with a diagonal Cone under orthogonal Constraint, referred to asMC(2). Its core idea is to embed and alignmultiple social networks in the common subspace via an unsupervised approach. Specifically, inMC(2) model, we first design amatrix optimization to infer the common subspace from different social networks. To address the nonconvex optimization, we then design an efficient alternating algorithm by leveraging its inherent functional property. Through extensive experiments on realworld datasets, we demonstrate that the proposed MC2 model significantly outperforms the state-of-the-art methods.
Keyword:
Social network alignment
unsupervised learning
matrix factorization
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6.6
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1.5K
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