Neighborhood Interaction Attention Network for Link Prediction
Proceedings of the 28th ACM International Conference on Information and Knowledge Management(2019)
摘要
Interactions between neighborhoods of two target nodes are often regarded as important clues for link prediction. In this paper, we propose a novel link prediction neural model named Neighborhood Interaction Attention Network (NIAN), which is able to automatically learn comprehensive neighborhood interaction features and predict links in an end-to-end way. The proposed model mainly consists of two attention layers. A node-level attention is designed to extract latent structure features of nodes in target neighborhoods. Based on the latent node features, a neighborhood-level attention is proposed to learn neighborhood interaction features by considering different importance of pair-wise interactions. The superiority of NIAN is demonstrated by extensive experiments on 6 benchmark datasets against 12 popular and state-of-the-art approaches.
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关键词
attention network, link prediction, neighborhood interaction
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