Probing the influence maximization-cost minimization problem in social networks by using a multi-objective discrete differential evolution optimization

Influence maximization is to extract k most influential individuals that can maximize the spreading coverage of the promoted information in social networks. The existing majority of efforts merely focus on how to maximize the coverage of the promotion. Whereas the disposable budget acts as a significant factor needed to be considered in practical scenarios. In this paper, we take into account both the influence maximization and cost minimization simultaneously in the perspective of variable cost for the activation of different candidate node with the size of targeted seed set fixed, and formulate the problem as a multi-objective optimization. A multi-objective discrete differential evolution optimization (MODDE) with mutation, crossover and selection operators specifically adaptable to the topological network structure is proposed. For the non-uniform cost setting for each node in the influence spreading process, a novel functional metric is designed to measure the cost of igniting each candidate node. The non-dominated solutions derived from MODDE can better balance the coverage of influence and budget costs, thus providing decision makers with more choices. Extensive experiments and statistic tests on real-world networks are performed to estimate the proposed method, and the results demonstrate the outperformance of the MODDE over the state-of-the-art methods. Additional Keywords and Phrases: Social network analysis, Influence maximization, Cost minimization, Multi-objective optimization, Differential evolution algorithm, Pareto optimal