Meta-path-based key node identification in heterogeneous networks

Abstract

Identifying key nodes in complex networks remains challenging. Whereas previous studies focused on homogeneous networks, real-world systems comprise multiple node and edge types. We propose a meta-path-based key node identification (MKNI) method in heterogeneous networks to better capture complex interconnectivity. Considering that existing studies ignore the differences in propagation probabilities between nodes, MKNI leverages meta-paths to extract semantics and perform node embeddings. Trust probabilities reflecting propagation likelihoods are derived by calculating embedding similarities. Node importance is calculated by using metrics incorporating direct and indirect influence based on trust. The experimental results on three real-world network datasets, DBLP, ACM and Yelp, show that the key nodes identified by MKNI exhibit better information propagation in the Susceptible Infected (SI) and susceptibility-influence model (SIR) model compared to other methods. The proposed method provides a reliable tool for revealing the topological structure and functional mechanisms of the network, which can guide more effective regulation and utilization of the network.