WSLC: Weighted semi-local centrality to identify influential nodes in complex networks

Abstract

Identifying and ranking influential nodes in complex networks is a critical aspect to study the survival and robustness of networks. Many ongoing researches have proposed centrality metrics to address this problem, so that the performance of each is attributed to specific scenarios. For example, metrics based on local structure have low ranking accuracy due to the use of limited information, and metrics based on global structure suffer from high complexity. Meanwhile, metrics based on semi-local structure are amazingly well, but an efficient centrality for identifying influential nodes is still not available due to differences in the structure and scale of networks. In addition, most semi-local centrality metrics only consider one aspect of each node's information, and their development still faces serious challenges. This paper develops a Weighted Semi-Local Centrality (WSLC) to identify influential nodes in complex networks based on extended neighborhood concept. Here, several different weights are investigated to find the best performance on WSLC. We use the extended neighborhood concept to select the nearest neighbors, which considers the global information of the network in a limited and efficient way to calculate the ranks. Here, a distributed approach is presented that can cut a subgraph of the entire network for each node with low complexity. This subgraph contains neighbors with different hops, which are used to maintain high efficiency when facing large-scale networks. In addition to the importance of the node itself, WSLC also combines the importance of the node's nearest neighbors with different hops for ranking. Therefore, defining semi-local structure with a distributed approach as well as using an efficient edge weighting policy differentiates WSLC from other existing centrality metrics. The evaluation of WSLC has been done through several real-world networks using Kendall's correlation. The effectiveness of WSLC under the SIR infection spreading model has been verified by extensive simulations compared to state-of-the-art centrality metrics.