基于节点传播特性的迭代资源分配算法
Iterative resource allocation based on propagation feature of node for identifying the influential nodes
摘要：
网络中有影响力节点的识别是网络中最有前途的研究领域之一。在本文中，我们提出了一种改进的迭代资源分配（IIRA）方法，考虑到邻居的中心性信息和扩展速率的影响，为目标节点。通过对4个真实网络的SIR模型的计算结果进行比较，表明该方法比传统的IRA方法能更准确地识别影响节点。特别是在Erdos网络中，当扩展率为0.12时，Kendall τ可以提高23%.在蛋白质网络中，当扩展率为0.08时，Kendall 'stau增强了24%.
英文摘要：
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 Erdos network, the Kendall's tau could be enhanced 23\% when the spreading rate is 0.12. In the Protein network, the Kendall's tau could be enhanced 24\% when the spreading rate is 0.08.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
