Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure

Abstract

The safety and robustness of the network have attracted the attention of people from all walks of life, and the damage of several key nodes will lead to extremely serious consequences. In this paper, we proposed the clustering H-index mixing (CHM) centrality based on the Hindex of the node itself and the relative distance of its neighbors. Starting from the node itself and combining with the topology around the node, the importance of the node and its spreading capability were determined. In order to evaluate the performance of the proposed method, we use Susceptible-Infected-Recovered (SIR) model, monotonicity and resolution as the evaluation standard of experiment. Experimental results in artificial networks and real-world networks show that CHM centrality has excellent performance in identifying node importance and its spreading capability.

A novel method based on node correlation to evaluate the important nodes in complex networks

“…P.L. Lu et al also proposed an extended H-index centrality based on local H-index centrality and clustering coefficient [17].…”

A novel indicator weight tuning method based on fuzzy theory in mobile social networks

“…If we purely take the links held by the node into consideration, the importance of node can be denoted by degree centrality. Degree centrality (DC) [21,22] is a typical method based on local information, and it holds that the influence of a node is reduced to the number of its neighbor nodes. In a social network, a node represents a person, an edge represents the friendship between them, so DC believes that the person with more friends is more important.…”