GA-ComFit: A Genetic Algorithm With Community Fitness Sharing Niching for Multimodal Opinion Maximization

In social network analysis, the opinion maximization (OM) problem aims to locate several nodes as a seed set, which starts the information propagation and achieves the most positive opinion of a social network. Considering the situation in practice that decision makers prefer having alternatives to make the final decisions, the multimodal OM (mOM) problem is derived in this paper. In our work, the mOM problem is formulated first, with the goal to identify multiple heterogeneous well-performed seed sets for the primal OM problem. Secondly, a genetic algorithm with a novel niche technique, GA-ComFit, is developed to solve the proposed mOM problem. In GA-ComFit, potential seed sets are encoded as individuals. Built on fitness sharing, the community-based fitness sharing niching technique, ComFit, hierarchically clusters individuals into multiple niches based on the community feature of each individual. As a result, the proposed GA-ComFit generates multiple heterogeneous seed sets as the solution for the mOM problem. Furthermore, a series of experiments conducted on real-world social networks demonstrate that the proposed GA-ComFit generally offers a set of multiple excellent heterogeneous seed sets for the mOM problem. To the best of our knowledge, this is the first study of the OM problem from the multimodal optimization perspective.