A new algorithm for positive influence maximization in signed networks

Abstract

With the rapid development of online social networks, the problem of influence maximization (IM) has attracted much attention from researchers and has been applied in many areas such as marketing and finance. Since positive and negative relations may exist between individuals in social networks, the problem of influence maximization in signed networks has a wide range of applications. This paper presents an efficient algorithm for positive influence maximization in signed networks in the independent cascade model. First, we propose an independent path-based algorithm to compute the activation probabilities between the node pairs. Based on the activation probability, we define a propagation increment function to avoid simulating the influence spreading for selecting candidate seed nodes. We present an algorithm to select the seed nodes to obtain the largest positive influence spreading in the signed network. Empirical results in social networks show that our algorithm can have wider positive influence spreading than other methods.