Rumour spread minimization in social networks: A source-ignorant approach

Abstract

The spread of rumours in social networks has become a significant challenge in recent years. Blocking so-called critical edges, that is, edges that have a significant role in the spreading process, has attracted lots of attention as a means to minimize the spread of rumours. Although the detection of the sources of rumour may help identify critical edges this has an overhead that source-ignorant approaches are trying to eliminate. Several source-ignorant edge blocking methods have been proposed which mostly determine critical edges on the basis of centrality. Taking into account additional features of edges (beyond centrality) may help determine what edges to block more accurately. In this paper, a new source-ignorant method is proposed to identify a set of critical edges by considering for each edge the impact of blocking and the influence of the nodes connected to the edge. Experimental results demonstrate that the proposed method can identify critical edges more accurately in comparison to other source-ignorant methods.