SWIM

Abstract

A considerable amount of research has been devoted to the proposition of scalable algorithms for influence maximization. A number of such scalable algorithms exploit the community structure of the network. Besides the community structure, real-world social networks possess a different property, known as the layer structure. In this article, we propose a method based on the layer structure to maximize the influence in huge networks. Conducting experiments on a number of real-world networks, we will show that our method outperforms the state-of-the-art algorithms by its time complexity while having similar or slightly better final influence spread. Furthermore, unlike its predecessors, our method is able to show a high entanglement between structure and dynamics by giving insight on the reason why different networks have two contrasting behaviors in their saturation. By “saturation,” we mean a state during the seed selection process after which adjoining new nodes to the initial set will have a negligible effect on increasing the influence spread. We will demonstrate that how our method can predict the saturation dynamics in the networks. This prediction can be used to identify the network structures that are more vulnerable to the fast spread of the rumors.