加权有向网络中心节点识别的分解算法研究

Decomposition algorithm of center node in directed-weighted network

目前复杂网络节点重要性识别算法主要集中在无权、无向网络上不能全面地描述真实世界复杂网络的情况。大部分中心性度量方法仅仅考虑单一指标,忽略了节点出度与入度的差异,且忽视了权重的重要性。基于有向加权复杂网络,综合考虑节点出度与入度的差异,以及权值在真实网络中的实际重要性,提出了一种基于出度、入度和权值的中心节点识别算法——cw-壳分解算法。为了验证该算法的有效性,利用W-SIR传播模型在真实复杂网络上进行病毒传播仿真实验,结果表明cw-壳分解方法能够有效地对节点进行分级排序,识别出具有高扩散能力的节点。

The existing evaluation methods for node importance in complex network mainly focus on un-directed and unweighed networks,and can’t describe the real-world network completely. Most centrality measures only consider a single indicator,ignoring the difference between out-degree and in-degree of the node,and neglecting the importance of weight. Based on the directed-weighted complex network,this paper proposed a center node recognition algorithm,cw-shell decomposition method,which was based on out-degree,in-degree and weight,considering the difference between out-degree and in-degree of the node,and the practical importance of weight in real network. In order to verify the effectiveness of the new index,it used the weighted-susceptible-infectious-recovered model to simulate spreading process on real-world networks. The results show that the cw-shell decomposition method can rank the nodes efficiently,and identify the nodes with higher diffusion ability.