SpreadMax: A Scalable Cascading Model for Influence Maximization in Social Networks

Abstract

The rumor spreading and influence maximization are two important forms of social network communication. Many social network applications utilize influence maximization for finding the seed node. The most common approach for influence maximization is greedy, which delivers an optimal solution. However, the greedy approach has high computational overhead and offers a limited spreading capability. We address this problem by proposing a novel framework, SpreadMax, for maximizing the spreading process with reduced computational overhead. Our model consists of two phases. In the first phase, we identify seed nodes using hierarchical reachability approach and these designated seed nodes spread infection during the second phase. The proposed model is validated through experiments using real-world data, in comparison with existing methods. We observed that the SpreadMax technique performs better in maximizing the influence.