基于多阶邻居传播度量和拓扑特征的高影响力节点识别
摘要
How to quantitatively evaluate the influence of nodes in complex networks is an important research topic because it helps to understand the structure and functionality of networks. Most existing methods are primarily based on analyzing the network's inherent topology, needing more comprehensive utilization of propagation properties of multi-order neighboring nodes and topological information, significantly impacting identifying influential nodes. To this end, this paper proposes an algorithm called multi-order neighbor propagation metrics and topological features (MNPMTF)to effectively identify influential nodes in complex networks. Firstly, the algorithm combines a propagation model and shortest paths to characterize the propagation probability of neighboring nodes, thereby quantifying the likelihood of information spreading between nodes. Secondly, the algorithm considers the neighbor overlap ratio among multi-order neighbors to form the neighbor overlap degree, thereby quantifying the propagation paths of information in the neighbor network. Thirdly, the algorithm utilizes the k-shell index, h-index, and clustering coefficient to form a new index called the KHC coefficient, which describes the topological features of nodes. Finally, the algorithm integrates the propagation probability, neighbor overlap degree, and topological features within a 3-order neighbor range to evaluate the influence of nodes. Extensive experiments on nine entire networks demonstrate that the proposed algorithm outperforms seven representative methods regarding ranking accuracy, effectiveness, and discriminability, providing a new approach for assessing node influence in complex networks.