Fuzzy sign-aware diffusion models for influence maximization in signed social networks

Abstract

The diffusion models in the influence maximization problem are a trending topic in many companies' viral marketing to raise their business promotion. The majority of existing diffusion models have focused only on trust relationships, and a few models have considered distrust relationships. Nevertheless, the latter models lack appropriate theoretical support to cover social influences resulting from different user-relationship types and still do not provide proper predictions. In this study, a fuzzy-based approach is first introduced to model the influence propagation for different user-relationship types. Then, four novel fuzzy sign-aware diffusion models named FSC-SB, FSC-N, FST-SB, and FST-N are proposed by the introduced fuzzy-based approach in two categories: cascade and threshold-based models. In the proposed models, the user-relationship type is determined by a fuzzy expert system in which a natural multi-trust level relationship is applied instead of a commonly used crisp relationship. Moreover, new rules and equations are defined to determine a user's state by information received from its active neighbors. The performance of proposed models was compared with some state-of-the-art models conducted by two real-world networks, Bitcoin OTC and Bitcoin Alpha. The experimental results showed that the proposed models enhance the prediction accuracy and make effective decisions in viral marketing.