Suppressing Information Diffusion via Link Blocking in Temporal Networks

  • Xiu-Xiu Zhan
  • Alan Hanjalic
  • Huijuan WangEmail author
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 881)


In this paper, we explore how to effectively suppress the diffusion of (mis)information via blocking/removing the temporal contacts between selected node pairs. Information diffusion can be modelled as, e.g., an SI (Susceptible-Infected) spreading process, on a temporal social network: an infected (information possessing) node spreads the information to a susceptible node whenever a contact happens between the two nodes. Specifically, the link (node pair) blocking intervention is introduced for a given period and for a given number of links, limited by the intervention cost. We address the question: which links should be blocked in order to minimize the average prevalence over time? We propose a class of link properties (centrality metrics) based on the information diffusion backbone [19], which characterizes the contacts that actually appear in diffusion trajectories. Centrality metrics of the integrated static network have also been considered. For each centrality metric, links with the highest values are blocked for the given period. Empirical results on eight temporal network datasets show that the diffusion backbone based centrality methods outperform the other metrics whereas the betweenness of the static network, performs reasonably well especially when the prevalence grows slowly over time.


Link blocking Link centrality Information diffusion backbone Temporal network SI spreading 



This work has been partially supported by the China Scholarship Council (CSC).


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Electrical Engineering, Mathematics, and Computer ScienceDelft University of TechnologyDelftThe Netherlands

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