Dynamic node influence tracking based influence maximization on dynamic social networks

Abstract

Influence maximization is one of the popular problems in social network analysis. Its application includes viral marketing, epidemic control, and recommender systems. Most of the existing methods are applicable to static networks. However, many real-world networks are dynamic and they evolve with time. This paper studies influence maximization on dynamic social networks and proposes Dynamic Influence based Seed Selection (DYISSE) method. To find ‘k’ most influential nodes, the proposed method first estimates the influence of each node by introducing a Two-hop Triangular Influence (TTI) that measures the influence strength of each node by utilizing the property of triangles. Based on the TTI, a method named the Dynamic Tractable Set (D-TeSt) is proposed to track the changes in the influence of individual nodes when the topology of the network changes with time. The performance of the DYISSE method is analyzed under the LAIC model on two real-world and twelve synthetic dynamic networks. The results show that the proposed method performs better than the temporal versions of DegreeDiscount, MaxDegree, K-shell, Random, and Closeness centrality measures in extracting initial influential nodes.