Opinion evolution in society with stubborn agents using Temporal Model for Opinion and Triad Network Formation (TMOTNF)

Information diffusion is an important part of the social network. Information flows between the individuals in the social networks to shape and update their opinions about various topics. The updated opinion values of them further spread the information in the network. The social network is always evolving by nature, leading to the dynamics of the network. Connections keep on changing among the individuals based on the various characteristics of the networks and individuals. Opinions of individuals may again be affected by the changes in the network which leads to dynamics on the network. Therefore, the co-evolving nature of dynamics on/of the network is proposed. Co-evolving Temporal Model for Opinion and Triad Network Formation is modelled to evaluate the opinion convergence. Some fully stubborn agents are chosen in the network to affect opinion evolution, framing society’s opinion. It is also analysed how these agents can divert the whole network towards their opinion values. When temporal modelling is done using all the three conditions, Triadic Closure, Opinion Threshold value and the Page Rank value over the network, the network does not reach consensus at the convergence point. Various individuals with different opinion values still exist.

A model of indirect contagion based on a news similarity network

Our objective is to model indirect contagion among companies. We use pieces of news about companies to measure the similarities between them. We assume that two companies are similar if we may associate a story about one company with the story about another company and vice-versa. First, after statistically eliminating arbitrary similarities between companies, we introduce a network based on the news similarities. Second, we evaluate a vector of stationary probabilities associated with the process of contagion that takes place in the network and we use these pieces of information to define the notion of centrality. Third, we use this vector of stationary probabilities to build an associated graph that reveals the most important paths of information contagion. Finally, we build a model of indirect contagion and evaluate the size of the information avalanches that take place in the similarity network.

Normalized Laplace operators for hypergraphs with real coefficients

Chemical hypergraphs and their associated normalized Laplace operators are generalized and studied in the case where each vertex–hyperedge incidence has a real coefficient. We systematically study the effect of symmetries of a hypergraph on the spectrum of the Laplacian.