基于度与K-核的创新扩散网络节点传播能力度量
首发时间:2016-06-03
摘要:创新扩散网络中的节点传播能力度量具有十分重要的意义。节点的传播能力不仅取决于节点的局部信息也受其网络位置影响,本文综合节点度和K-核信息提出了新的节点传播能力度量方法。实证数据集上的仿真结果表明,采用该方法的Kendall's Tau相对于度和K-核方法最大分别可以提高6.80%和13.61%。本文提出的方法时间复杂度低,也适合于大规模网络的节点创新传播能力度量。
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Ranking node spreading influence based on degree and K-shell method in innovation diffusion network
Abstract:Ranking node spreading influence is of significance for innovation diffusion. We argue that the node spreading influence not only depend on the location of the node, but also rely on the local information. In this paper we propose an improved method integrating the node degree and K-shell value to rank the node spreading influence in innovation diffusion. The simulation result on four real network shows that, comparing with degree and K-shell method the largest improved ratio of improved method could reach 6.80% and 13.61% respectively. Furthermore, our method also could be used to rank the node spreading influence in large scale network because of its low computational cost.
Keywords: Node spreading influence Innovation diffusion degree centrality K-shell method
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