Abstract
Dynamic network link prediction is extensively applicable in various scenarios, and it has progressively emerged as a focal point in data mining research. The comprehensive and accurate extraction of node information, as well as a deeper understanding of the temporal evolution pattern, are particularly crucial in the investigation of link prediction in dynamic networks. To address this issue, this paper introduces a node representation learning framework based on Graph Convolutional Networks (GCN), referred to as GCN_MA. This framework effectively combines GCN, Recurrent Neural Networks (RNN), and multi-head attention to achieve comprehensive and accurate representations of node embedding vectors. It aggregates network structural features and node features through GCN and incorporates an RNN with multi-head attention mechanisms to capture the temporal evolution patterns of dynamic networks from both global and local perspectives. Additionally, a node representation algorithm based on the node aggregation effect (NRNAE) is proposed, which synthesizes information including node aggregation and temporal evolution to comprehensively represent the structural characteristics of the network. The effectiveness of the proposed method for link prediction is validated through experiments conducted on six distinct datasets. The experimental outcomes demonstrate that the proposed approach yields satisfactory results in comparison to state-of-the-art baseline methods.