Influence Spread Evaluation and Propagation Rebuilding

Abstract

In social networks, studies about influence maximization mainly focus on the algorithm of finding seed nodes, but ignore the intrinsic properties of influence propagation. In this paper, we consider the relationship between seed sets & influence spread. For static propagation, we reasonably abstract the relationship between the size of the seed set and the influence spread in influence maximization problem as a logarithmic function. We also provide experiments on large collaboration networks, showing the rationality and the accuracy of the proposed function. For dynamic influence propagation, we rebuild it as a continuous linear dynamical system called 3DS, which is based on Newton's law of cooling. Furthermore, we give an efficient method to compute the influence spread function of time without much loss of accuracy. Its efficiency is demonstrated by complexity analysis.