Identifying Influential Spreaders in Complex Networks with Probabilistic Links

Abstract

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.