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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Temporal similarity metrics for latent network reconstruction: The role of time-lag decay
Organization Unit
  • Hao Liao
  • Ming-Kai Liu
  • Manuel Mariani
  • Mingyang Zhou
  • Xingtong Wu
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title Information Sciences
Publisher Elsevier
Geographical Reach international
ISSN 0020-0255
Volume 489
Page Range 182 - 192
Date 2019
Abstract Text 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.
Digital Object Identifier 10.1016/j.ins.2019.01.081
Other Identification Number merlin-id:17706
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