Identifying influential spreaders in complex networks based on limited spreading domain

Abstract

In recent years, the problem of influential spreader identification in complex networks has attracted extensive attention as its fundamental role in social network analysis, rumor controlling, viral marketing and other related fields. Centrality measures that consider different scales of neighborhood are commonly utilized for ranking node influence. The 2-hop neighborhood of the target node is deemed a suitable evaluation metric. However, as the network scale expands, only considering 2-hop neighborhood is overly restrictive. Furthermore, the interconnections among nodes are often disregarded. In this article, a new method named Limited Spreading Domain (LSD) is proposed to identify influential spreaders. LSD defines the target node’s 2-hop neighborhood as basic domain and takes the neighbors who are 3–6 hops away from target node as extended domain. The influence of target node is modeled as diffusion along the paths with limited length in basic domain and extended domain. A series of experiments are conducted in eight real complex networks and results demonstrate that LSD outperforms common centralities in terms of accuracy, stability,distinguishability and scalability.