IDR: Positive Influence Maximization and Negative Influence Minimization Under Competitive Linear Threshold Model

In influence maximization problem, we would like to find an initial subset of nodes in a given graph, which maximizes the final number of affected nodes through "word of mouth" propagation. Measuring the influence spread of set of seed nodes and gradually selecting the node with largest marginal increase is one of the main approaches of existing algorithms. In this paper, we try to solve this problem from a different strategy — in an improvement perspective. A more complex condition is depicted as both positive and negative opinions are propagating in the social network. The objective function considers the maximization of the positive influence and minimizes the negative opinion spreading simultaneously. We propose IDR (Influence Distribution Redirection) algorithm to define initial seed nodes of influence diffusion based on redirecting the influence distribution of nodes to maximize the objective function. The influence distribution of nodes shows the potential influence trend of nodes during the influence diffusion process. The key strategy is reducing the positive influence nearby the steady nodes and increasing in the vacillate region. From the experimental results, IDR outperforms the compared method on the objective function. In addition, IDR also improves the performance of increasing the number of positive active nodes and decreasing the number of negative activated nodes respectively.