Open AccessNode importance measurement based on the degree and clustering coefficient information
Author(s) -
Ren Zhuo-Ming,
Feng Shao,
Jianguo Liu,
Qiang Guo,
Bing–Hong Wang
Publication year - 2013
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.62.128901
Subject(s) - clustering coefficient , cluster analysis , closeness , robustness (evolution) , computer science , degree (music) , data mining , node (physics) , measure (data warehouse) , topology (electrical circuits) , mathematics , artificial intelligence , physics , mathematical analysis , biochemistry , chemistry , acoustics , gene , quantum mechanics , combinatorics
The node importance measurement plays an important role in analyzing the robustness of the network. Most researchers use the degree or clustering coefficient to measure the node importance. However, the degree can only take into account the neighbor size, regardless of the clustering property of the neighbors. The clustering coefficient could only measure the closeness among the neighbors and neglect the activity of the target node. In this paper, we present a new method to measure the node importance by combining neighbor and clustering coefficient information. The robustness results measured by the network efficiency through removing the important nodes for the US Air network, the power grid of the western United States and Barabasi-Albert networks show that the new method can more accurately evaluate the node importance than the degree, neighbor information and k-shell indices.