Link Prediction with Attention-Based Semantic Influence of Multiple Neighbors

The establishment of social links is not only determined by personal interests but also by neighbors’ influences, which may vary across different neighbors. However, the independent influence of each neighbor has not been separately considered on semantic level in current approaches. In this work, we predict missing social links by modeling semantic influence of each neighbor separately with an embedding approach. The semantic of influence is fine grained on each neighbor’s specific interest with attention-based method. The proposed model named AIMN (Attention-based semantic Influence of Multiple Neighbors) is integrated with structure information with a uniform framework. Extensive experiments on different real-world networks demonstrate that AIMN outperforms state-of-the-art methods.