关键词: 复杂网络, k核分解方法, 精准k核, 混合中心性, 节点重要性

Abstract: Critical nodes are the core elements of complex networks and can play a critical role in maintaining structural stability and information transmission. The k-shell is a common measure index of node importance. But due to its shortcomings such as destroying the overall structure information of the network and ignoring the influence of neighboring nodes, it is difficult to ensure that each node can be quantitatively distinguished. In order to improve the accuracy of node identification, this paper firstly improves the decomposition process of k-shell and proposes the Ak (accurate k-shell). Considering the influence of local feature information and global structure information on nodes in the network, the Ak is applied to the gravity centrality and the AGC (accurate gravity centrality) is proposed, subsequently. Because of the good expansibility for Shannon entropy in informatics has good expansibility in the identification of key nodes in the network,the MC (mixed centrality) is finally proposed to evaluate the importance of nodes pluralistically by combining Shannon entropy of neighborhood centrality, neighborhood Ak and AGC. Under 7 kinds of real networks, a series of experiments on the monotonicity and accuracy of MC and other node evaluation indexes in terms of monotonicity and accuracy were conducted. The experimental results show that MC has better performance in identifying key nodes.

Key words: complex network, k-shell decomposition method, accurate k-shell, mixed centrality, node importance