MEGA: identifying influential nodes in heterogeneous networks based on meta-path and attention

Abstract Identifying influential nodes in complex networks is crucial for applications like information diffusion, rumor propagation, and epidemic spreading. Existing methods often assume uniform importance among neighbor nodes or fail to account for the structural and semantic diversity of relationships in heterogeneous networks. This leads to suboptimal identification of influential nodes, particularly in complex, attribute-rich networks. To address these shortcomings, a meta-path embedding-based graph attention network model is proposed, namely MEGA. MEGA is utilized to identify influential nodes in heterogeneous networks. This paper attempts to identify the influential set of author nodes in academic networks, defined as the set of nodes whose influence maximizes the spread of information. MEGA is described as follows. Firstly, the required meta-paths are defined, and node embeddings are obtained using a meta-path embedding method. Then, the subgraph networks corresponding to each meta-path network are generated based on random walks. Further, the graph attention network is employed to assign different weight values to neighbor nodes within the subgraph networks, indicating the varying influence of nodes on their neighbor nodes. Finally, the influence of nodes in the subgraph network is calculated using entropy, and the influence values from all subgraphs are aggregated to obtain the final influence value of each node. The effectiveness of MEGA is validated using analysis metrics from the independent cascade model. Experimental results demonstrate MEGA exceeds six baseline methods in terms of the influence spread across three datasets.