Identifying Influential Nodes in Complex Networks From Semi-Local and Global Perspective
W.D. Liu, Pengli Lu, T. Zhang
Abstract
How to accurately identify influential nodes in complex networks remains a challenge due to the increasing network scale, complex topology, and dynamic network behaviors. Although a variety of approaches have been proposed, most researchers have only considered single or limited dimensions of nodes to varying degrees. In this article, a new centrality model [semi-local and global centrality (SLGC)] built on the semi-local and global structure of the node is proposed, which can capture a larger scope and richer information to evaluate how crucial a node is. First, the generalized energy is defined based on the generalized matrix, and then, the first-order and second-order generalized energy entropies are constructed by integrating information entropy and generalized energy to reflect semi-local influence (SLI). Second, the global influence (GI) is constructed based on the clustering coefficients of nodes and the distance between nodes, and finally, the total influence of nodes is derived from the above two aspects. The SLGC is contrasted with seven benchmark methods on nine real networks to assess the algorithm’s performance, and the data indicated that the SLGC has good effectiveness and versatility in monotonicity, resolution, accuracy, and top-10 nodes.