High-Order Degree and Combined Degree in Complex Networks

被引:6
|
作者
Wang, Shudong [1 ]
Wang, Xinzeng [2 ]
Song, Qifang [2 ]
Zhang, Yuanyuan [3 ]
机构
[1] China Univ Petr, Coll Comp & Commun Engn, Qingdao 266580, Peoples R China
[2] Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao 266510, Peoples R China
[3] Qingdao Univ Technol, Comp Engn Inst, Qingdao 266520, Peoples R China
基金
中国国家自然科学基金;
关键词
FASTER ALGORITHM; SOCIAL NETWORKS; RANDOM-WALKS; CENTRALITY; SPREADERS; GRAPHS;
D O I
10.1155/2018/4925841
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
We define several novel centrality metrics: the high-order degree and combined degree of undirected network, the high-order out-degree and in-degree and combined out out-degree and in-degree of directed network. Those are the measurement of node importance with respect to the number of the node neighbors. We also explore those centrality metrics in the context of several best-known networks. We prove that both the degree centrality and eigenvector centrality are the special cases of the high-order degree of undirected network, and both the in-degree and PageRank algorithm without damping factor are the special cases of the high-order in-degree of directed network. Finally, we also discuss the significance of high-order out-degree of directed network. Our centrality metrics work better in distinguishing nodes than degree and reduce the computation load compared with either eigenvector centrality or PageRank algorithm.
引用
收藏
页数:12
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