Temporal information gathering process for node ranking in time-varying networks

Journal article (2019)

Authors

C. Qu Shandong University
Guanghui Wang Shandong University
Jianliang Wu Shandong University
Zi Ke Zhang Hangzhou Normal University, Ministry of Education Hangzhou
Copyright: © 2019 C. Qu, X. Zhan, Guanghui Wang, Jianliang Wu, Zi-ke Zhang
DOI: https://doi.org/10.1063/1.5086059
Graph theory Data mining Social networks Covariance and correlation
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Published Date

2019

Language

English

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Abstract

Many systems are dynamic and time-varying in the real world. Discovering the vital nodes in temporal networks is more challenging than that in static networks. In this study, we proposed a temporal information gathering (TIG) process for temporal networks. The TIG-process, as a node's importance metric, can be used to do the node ranking. As a framework, the TIG-process can be applied to explore the impact of temporal information on the significance of the nodes. The key point of the TIG-process is that nodes' importance relies on the importance of its neighborhood. There are four variables: temporal information gathering depth n, temporal distance matrix D, initial information c, and weighting function f. We observed that the TIG-process can degenerate to classic metrics by a proper combination of these four variables. Furthermore, the fastest arrival distance based TIG-process (fad-tig) is performed optimally in quantifying nodes' efficiency and nodes' spreading influence. Moreover, for the fad-tig process, we can find an optimal gathering depth n that makes the TIG-process perform optimally when n is small.