Neighborhood Interaction Attention Network for Link PredictionOpen Website

Published: 01 Jan 2019, Last Modified: 12 May 2023CIKM 2019Readers: Everyone
Abstract: 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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