SIMiner: a stigmergy-based model for mining influential nodes in dynamic social networks

With the widespread of the Internet, the on-line social network with big data is rapidly developing over time. Many enterprises attempt to develop their business by utilizing the power of on-line social networking platforms. A considerable amount of work has focused on how to select a set of influential users to maximize a kind of positive influence in static social networks. However, networks evolve, and the topological structure changes over time. How to mine and adapt the influencers in a dynamic and large-scale environment becomes a challenging issue. In this paper, a collective intelligence model, i.e., stigmergy-based influencers miner, is proposed to investigate influential nodes in a fully dynamic environment. The proposed model is capable of analysing influential relationships in a social network in decentralized manners and identifying the influencers more efficiently than traditional seed selection algorithms. Moreover, it is capable of adapting the solutions in complex dynamic environments without any interruptions or recalculations. Experimental results show that the proposed model achieves better performance than other traditional models in both static and dynamic social networks by considering both efficiency and effectiveness.