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Predicting the evolution of complex networks via similarity dynamics

Tao Wu, Leiting Chen, Linfeng Zhong and Xingping Xian

Physica A: Statistical Mechanics and its Applications, 2017, vol. 465, issue C, 662-672

Abstract: Almost all real-world networks are subject to constant evolution, and plenty of them have been investigated empirically to uncover the underlying evolution mechanism. However, the evolution prediction of dynamic networks still remains a challenging problem. The crux of this matter is to estimate the future network links of dynamic networks. This paper studies the evolution prediction of dynamic networks with link prediction paradigm. To estimate the likelihood of the existence of links more accurate, an effective and robust similarity index is presented by exploiting network structure adaptively. Moreover, most of the existing link prediction methods do not make a clear distinction between future links and missing links. In order to predict the future links, the networks are regarded as dynamic systems in this paper, and a similarity updating method, spatial–temporal position drift model, is developed to simulate the evolutionary dynamics of node similarity. Then the updated similarities are used as input information for the future links’ likelihood estimation. Extensive experiments on real-world networks suggest that the proposed similarity index performs better than baseline methods and the position drift model performs well for evolution prediction in real-world evolving networks.

Keywords: Link prediction; Evolutionary dynamics; Spatial–temporal position drift; Network evolution (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:465:y:2017:i:c:p:662-672

DOI: 10.1016/j.physa.2016.08.013

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Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

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