Abstract—Link prediction is an important task in the area of complex networks. Some networks can be better modeled by temporal networks where the patterns of link appearance and disappearance varying with time. However, most of the previous link prediction researches ignore the temporal behaviors of links. The temporal link prediction needs to predict future links via a known network, considering the temporal relationship of node pairs. We propose a method combining the node centrality with time series. We distinguish the contributions of common neighbors to link generation by their centralities. Compared with benchmark approaches in several temporal networks, the proposed method can improve the accuracy of temporal link prediction efficiently.
Index Terms—Temporal link prediction, eigenvector centrality, time series, temporal networks.
The authors are with the School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu, China (e-mail: zhangkun@njust.edu.cn).
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Cite: Ting Zhang, Kun Zhang, Laishui Lv, and Xun Li, "Temporal Link Prediction Using Node Centrality and Time Series," International Journal of Future Computer and Communication vol. 9, no. 3, pp. 62-65, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
