基于共同邻居数的重要节点发现算法

Identifying Influential Nodes in Large-scale Networks Based on Neighbor Classification

识别重要节点一直是复杂网络下的热点问题之一,因为识别出的重要节点能够在人群中的信息传播或疾病免疫中起到重要作用。目前大量的方法研究基本上是从节点的邻居信息、网络中的最短路径和节点删除这3个角度出发。现有的基于节点邻居信息的方法并没有对邻居节点的作用做出具体的说明,也没有对邻居节点的贡献在不同维度上进行区分。本文提出一种SCCN方法,该方法将邻居节点的贡献分为加强该节点所在的连接紧密的本地区域内的传播效果和扩展该节点所携带的信息至网络其他区域2个部分。通过标准SIR模型来评价SCCN的表现,并在8个真实网络上与度中心性、K-shell、介数中心性和PageRank比较。实验结果表明,SCCN具有更高的准确性和稳定性以及较低的时间复杂度,能够应用于大规模网络中。

Identifying important nodes has always been one of the hot problems under complex networks,because the identified important nodes can play an important role in information dissemination or disease immunization in the population.The research of large number of current methods is basically based on three perspectives:node’s neighbor information,shortest path in the network and node deletion.The existing approaches based on the node’s neighbor information do not provide a specific descrip⁃tion of the role of neighboring nodes and do not differentiate the contributions of neighboring nodes in different dimensions.This paper proposes a SCCN method,this method divides the contribution of neighbor nodes into two parts:strengthening the propaga⁃tion effect within the tightly connected local area where the node is located and extending the information carried by the node to other areas of the network.The performance of SCCN is evaluated by the standard SIR model and compared with degree central⁃ity,K-shell,meso-centrality and PageRank on eight real networks.The experimental results show that SCCN has higher accu⁃racy and stability,as well as lower time complexity,and can be applied to large-scale networks.