利用KSN算法发现网络中有影响力的结点

准确高效地发现网络中有影响力的传播者具有非常重要的理论和现实意义。近年来,结点影响力排序受到了多领域学者的广泛关注。K-shell是一种较好的结点影响力评价指标；然而,仅仅依赖结点自身K-shell值实现的算法通常具有评估结果精确度不高、适用性较差等缺陷。针对此问题,提出KSN(the K-shell and neighborhood centrality)中心性模型,该算法综合考虑了结点本身及其所有二阶以内邻居结点的K-shell值。实验结果表明,所提出算法 度量结点传播的能力 比度中心性、介数中心性、K-shell分解、混合度分解等方法更准确。

Detecting Most Influential Nodes in Complex Networks by KSN Algorithm

It is very important in theory and practice to detect influential spreaders in networks accurately and efficiently.Recently,scholars from various fields have paid their attention to the study of ranking nodes.The K-shell index is a relatively powerful indicator to estimate the spreading ability of nodes.However,due to only attributes of the node itself being considered,limitation in accuracy and universal applicability will exist for K-shell decomposition.To solve this problem,this paper proposed a novel algorithm called KSN (the K-shell and neighborhood centrality) to estimate the spreading influence of a node by its K-shell value and the K-shell indexes of its nearest and next nearest neighbors.Experimental results demonstrate that this proposed algorithm acts more precisely in detecting the most influential nodes than degree centrality,betweenness centrality,K-shell decomposition and the mixed degree decomposition,et al.