Xiangming Ni
ABSTRACT
In the actual world, many networks and graphs are fundamentally dynamic and heterogeneous. They contain many kinds of nodes and relations, and they are evolving with time. However, the majority of conventional link prediction techniques are currently limited to static or homogeneous networks and have the drawbacks of not fully exploiting the networks' time-domain evolutionary information as well as their extensive semantic and structural characteristics. In this paper, we propose a link prediction method (Att-ConvLSTM) that uses hierarchical attention to learn heterogeneous information and combines recurrent neural networks with temporal attention to capture evolutionary patterns. The proposed approach is found to be superior in AUC and Precision after comparison and analysis with various types of link predicting algorithms.
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Index Terms
- Dynamic Heterogeneous Link Prediction Based on Hierarchical Attention Model
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