Iterative Neighbour-Information Gathering for Ranking Nodes in Complex Networks

Shuang Xu, Pei Wang, Jinhu Lü

科研成果: 期刊稿件文章同行评审

23 引用 (Scopus)

摘要

Designing node influence ranking algorithms can provide insights into network dynamics, functions and structures. Increasingly evidences reveal that node's spreading ability largely depends on its neighbours. We introduce an iterative neighbourinformation gathering (Ing) process with three parameters, including a transformation matrix, a priori information and an iteration time. The Ing process iteratively combines priori information from neighbours via the transformation matrix, and iteratively assigns an Ing score to each node to evaluate its influence. The algorithm appropriates for any types of networks, and includes some traditional centralities as special cases, such as degree, semi-local, LeaderRank. The Ing process converges in strongly connected networks with speed relying on the first two largest eigenvalues of the transformation matrix. Interestingly, the eigenvector centrality corresponds to a limit case of the algorithm. By comparing with eight renowned centralities, simulations of susceptible-infected-removed (SIR) model on real-world networks reveal that the Ing can offer more exact rankings, even without a priori information. We also observe that an optimal iteration time is always in existence to realize best characterizing of node influence. The proposed algorithms bridge the gaps among some existing measures, and may have potential applications in infectious disease control, designing of optimal information spreading strategies.

源语言英语
文章编号41321
期刊Scientific Reports
7
DOI
出版状态已出版 - 24 1月 2017
已对外发布

指纹

探究 'Iterative Neighbour-Information Gathering for Ranking Nodes in Complex Networks' 的科研主题。它们共同构成独一无二的指纹。

引用此