基于LeaderRank和节点相似度的复杂网络重要节点排序算法

Node Ranking in Complex Networks Based on LeaderRank and Modes Similarity

复杂网络中重要节点对网络结构和功能的影响引起了广泛关注。本文在现有Leader Rank算法的基础上,利用节点相似度来衡量节点间的相互作用,建立了SRank算法进行重要节点排序。利用SIR传播模型和斯皮尔曼等级相关系数在真实社会网络数据上对本文算法与经典的重要节点排序算法进行仿真后,发现该算法在无向和有向网络中均具有更高的准确性。

The effect of important nodes in complex networks on the structure and function of the networks causes widespread concern. This paper presents a SRank algorithm based on LeaderRank and nodes similarity which is used to measure the interaction between nodes. The simulation of SIR model and Spearman＇s correlation coefficient on real social networks show that the SRankalgorithm preforms better on identifying influential nodes both in directed and undirected networks, compared with the other four classical algorithms.