Extracting Influential Nodes in Social Networks on Local Weight Aspect

Abstract

Studies have shown that influential nodes play an important role in all kinds of dynamic behavior in the complex network. Excavation or recognition of such nodes contributes to the development of application areas such as social network advertising and user interest recommendation. Although some heuristic algorithms such as degree, betweenness, closeness and k-shell (or k-core) can identify influential nodes at the same time, they are disadvantaged in terms of accuracy and time complexity. Based on this, the authors propose a novel local weight index to distinguish the node influence based on the theory of ties strength. This index emphasizes that the node influence is jointly decided by the quantity and quality of the neighbors, and its time complexity is much lower than closeness and betweenness. With the aid of SIR information transmission model, this paper verifies the validity of local weight index.