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User identity linkage across social networks is an essential problem forcross-network data mining. Since network structure, profile and contentinformation describe different aspects of users, it is critical to learneffective user representations that integrate heterogeneous information. Thispaper proposes a novel framework with INformation FUsion and NeighborhoodEnhancement (INFUNE) for user identity linkage. The information fusioncomponent adopts a group of encoders and decoders to fuse heterogeneousinformation and generate discriminative node embeddings for preliminarymatching. Then, these embeddings are fed to the neighborhood enhancementcomponent, a novel graph neural network, to produce adaptive neighborhoodembeddings that reflect the overlapping degree of neighborhoods of varyingcandidate user pairs. The importance of node embeddings and neighborhoodembeddings are weighted for final prediction. The proposed method is evaluatedon real-world social network data. The experimental results show that INFUNEsignificantly outperforms existing state-of-the-art methods.

A Novel Framework with Information Fusion and Neighborhood Enhancement for User Identity Linkage