Iterative resource allocation based on propagation feature of node for identifying the influential nodes

作者：Zhong, Lin-Feng^*; Liu, Jian-Guo; Shang, Ming-Sheng

来源：Physics Letters, Section A: General, Atomic and Solid State Physics , 2015, 379(38): 2272-2276.

DOI：10.1016/j.physleta.2015.05.021

• SPREADING INFLUENCE
• COMPLEX
• CENTRALITY
• RANKING
• NETWORK

The identification of the influential nodes in networks is one of the most promising domains. In this paper, we present an improved iterative resource allocation (IIRA) method by considering the centrality information of neighbors and the influence of spreading rate for a target node. Comparing with the results of the Susceptible Infected Recovered (SIR) model for four real networks, the IIRA method could identify influential nodes more accurately than the tradition IRA method. Specially, in the Eras network, Kendall's tau could be enhanced 23% when the spreading rate is 0.12. In the Protein network, Kendall's tau could be enhanced 24% when the spreading rate is 0.08.

• 出版日期2015-10-9
• 单位上海理工大学; 电子科技大学