School of Computer and Information Science, Southwest University, Chongqing, China.
PLoS One. 2013 Jun 14;8(6):e66732. doi: 10.1371/journal.pone.0066732. Print 2013.
How to identify influential nodes is a key issue in complex networks. The degree centrality is simple, but is incapable to reflect the global characteristics of networks. Betweenness centrality and closeness centrality do not consider the location of nodes in the networks, and semi-local centrality, leaderRank and pageRank approaches can be only applied in unweighted networks. In this paper, a bio-inspired centrality measure model is proposed, which combines the Physarum centrality with the K-shell index obtained by K-shell decomposition analysis, to identify influential nodes in weighted networks. Then, we use the Susceptible-Infected (SI) model to evaluate the performance. Examples and applications are given to demonstrate the adaptivity and efficiency of the proposed method. In addition, the results are compared with existing methods.
如何识别复杂网络中的关键节点是一个关键问题。度中心性简单易用，但无法反映网络的全局特征。介数中心性和接近中心性不考虑节点在网络中的位置，而半局部中心性、leaderRank 和 pageRank 方法只能应用于无权重网络。在本文中，提出了一种基于生物启发的中心性度量模型，将 Physarum 中心性与通过 K-壳分解分析得到的 K-壳指数相结合，用于识别加权网络中的关键节点。然后，我们使用易感染-感染 (SI) 模型来评估该方法的性能。通过示例和应用来说明所提出方法的适应性和效率，并与现有方法进行了比较。
