Identifying Influential Spreaders in Complex Networks with Probabilistic Links

  • Pavlos Basaras
  • Dimitrios KatsarosEmail author
Part of the Lecture Notes in Social Networks book series (LNSN)


Dynamic complex networks illustrate how “agents” interact by exchanging information in a constantly changing network. Typical examples of such networks are online social networks or human contacts. This article contemplates the common distribution of time that user-nodes spend on their activities, and describes a method for identifying real-time influential spreaders. We model the reciprocal activities of actor-nodes with probabilistic links and propose a technique for identifying influential spreaders in complex networks with probabilistic edges. The proposed measure, namely, ranged Probabilistic Communication Area (rPCA), is evaluated under the susceptible-infectious-removed (SIR) model, where the results illustrate that rPCA can detect very effective spreaders in a networked environment with probabilistic edges.



This work was supported by the project “REDUCTION: Reducing Environ-mental Footprint based on Multi-Modal Fleet management System for Eco-Routing and Driver Behaviour Adaptation”, funded by the EU.ICT program, Challenge ICT-2011.7.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of ThessalyVolosGreece

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