[KDD 2020] Unsupervised Differentiable Multi-aspect Network Embedding
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Network embedding is an inuential graph mining technique for,representing nodes in a graph as distributed vectors. However, the,majority of network embedding methods focus on learning a single vector representation for each node, which has been recently,criticized for not being capable of modeling multiple aspects of a,node. To capture the multiple aspects of each node, existing studies mainly rely on oine graph clustering performed prior to the,actual embedding, which results in the cluster membership of each,node (i.e., node aspect distribution) xed throughout training of the,embedding model. We argue that this not only makes each node,always have the same aspect distribution regardless of its dynamic,context, but also hinders the end-to-end training of the model that,eventually leads to the nal embedding quality largely dependent,on the clustering. In this paper, we propose a novel end-to-end,framework for multi-aspect network embedding, called,asp2vec,,,in which the aspects of each node are dynamically assigned based,on its local context. More precisely, among multiple aspects, we,dynamically assign a,single,aspect to each node based on its current,context, and our aspect selection module is end-to-end differentiable via the Gumbel-Softmax trick. We also introduce the aspect,regularization framework to capture the interactions among the,multiple aspects in terms of relatedness and diversity. We further,demonstrate that our proposed framework can be readily extended,to heterogeneous networks. Extensive experiments towards various,downstream tasks on various types of homogeneous networks and,a heterogeneous network demonstrate the superiority of,asp2vec,.

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