Geodesy

Quantum mechanics

World Wide Web

Benchmark (surveying)

Theoretical computer science

Data mining

Computer science

Mathematical optimization

Social network (sociolinguistics)

Thermodynamics

Physics

Distrust

Diffusion

Psychology

Mathematics

Geography

Social media

Psychotherapist

Artificial intelligence

Action (physics)

Maximization

Machine learning

Expert Systems with Applications

Influence maximization is an important issue in social network analysis domain which concerns finding the most influential nodes. Determining the influential nodes is made with respect to information diffusion models. Most of the existing models only contain trust relationships while distrust exist in social networks as well. There exist some drawbacks in limited studies where distrust relationship is involved. The most outstanding drawback is the lack of assessment on the validity of the schemes presented on how influence propagates through distrust relationships in comparison with real word propagation in social networks. In this paper, two schemes are proposed, where based on each, some new models are proposed in two classes: cascade-based and threshold-based. All models of concern here are evaluated in comparison with the benchmark models through two real data sets, the Epinions and Bitcoin OTC. Results obtained indicate the superiority of one of the proposed schemes: when a distrusted user performs an action or adopts an opinion, the target users may tend not to do it.

Assessing information diffusion models for influence maximization in signed social networks