Novel fake news spreading model with similarity on PSO-based networks

Abstract This paper proposes a fake news spreading model with similarity taken into account, assuming that the similarity between individuals can affect the transmission rate. Simulations show that the similarity of two connected nodes and the product of their degrees are positively correlated when the network temperature is small, and the similarity of two connected nodes decreases as the product of their degrees increases. Thus the transmission rate can be expressed as the function of their degrees in the proposed model. The theoretic analysis demonstrates the critical threshold is related to both the influence coefficient and the similarity function. Simulation results show a smaller influence coefficient leads to a larger critical threshold and smaller final density of stiflers.