Abstract
Identifying the most influential spreaders is important in optimizing the network structure or disseminating information through networks. Recent study showed that the K-truss decomposition could filter out the nodes that performed a worse spreading behavior in the maximal K-shell subgraph. The spreaders belonging to the maximal K-truss subgraph show better performance compared to previously used importance criteria. However, the accuracy of the K-truss or the K-shell in determining node coreness is largely susceptible to core-like group. In this paper, we propose an improved diffusion K-truss decomposition method by considering both the diffusion and clustering of edges to eliminate the impact of core-like group on identifying influential nodes. To validate the effectiveness of the proposed method, we compare it with five typical methods by carrying out Monte–Carlo simulations over six real complex networks. Simulation results demonstrate that the proposed method can effectively disintegrate the core-like group and accurately identify the influential nodes.
Published Version
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