Temporal similarity metrics for latent network reconstruction: The role of time-lag decay

• Published in: Information sciences Vol. 489; pp. 182 - 192
• Main Authors: Liao, Hao, Liu, Ming-Kai, Mariani, Manuel Sebastian, Zhou, Mingyang, Wu, Xingtong
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.07.2019
• Subjects: Information network; Innovation diffusion; Network reconstruction; Temporal similarity; Innovation diffusion; Information network; Temporal similarity; Network reconstruction
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2019.01.081

•We design and validate simple metrics for latent network reconstruction inspired by the literature on link prediction.•Temporal similarity metrics can be highly effective in latent network reconstruction and require small computational time.•An extensive benchmarking of the resulting temporal similarity metrics is provided on both social networks and networks of different nature. When investigating the spreading of a piece of information or the diffusion of an innovation, we often lack information on the underlying propagation network. Reconstructing the hidden propagation paths based on the observed diffusion process is a challenging problem which has recently attracted attention from diverse research fields. To address this reconstruction problem, based on static similarity metrics commonly used in the link prediction literature, we introduce new node-node temporal similarity metrics. The new metrics take as input the time-series of multiple independent spreading processes, based on the hypothesis that two nodes are more likely to be connected if they were often infected at similar points in time. This hypothesis is implemented by introducing a time-lag function which penalizes distant infection times. We find that the choice of this time-lag function strongly affects the metrics’ reconstruction accuracy, depending on the network’s clustering coefficient, and we provide an extensive comparative analysis of static and temporal similarity metrics for network reconstruction. Our findings shed new light on the notion of similarity between pairs of nodes in complex networks.