Novel Multidimensional Models of Opinion Dynamics in Social Networks
By Sergey E. Parsegov, Anton V. Proskurnikov, Roberto Tempo, Noah E. Friedkin. IEEE Transactions on Automatic Control, 2015
398 citations recorded by the source corpus.
Unlike many complex networks studied in the literature, social networks rarely exhibit unanimous behavior, or consensus. This requires a development of mathematical models that are sufficiently simple to be examined and capture, at the same time, the complex behavior of real social groups, where opinions and actions related to them may form clusters of different size. One such model, proposed by Friedkin and Johnsen, extends the idea of conventional consensus algorithm (also referred to as the iterative opinion pooling) to take into account the actors’ prejudices, caused by some exogenous factors and leading to disagreement in the final opinions. In this paper, we offer a novel multidimensional extension, describing the evolution of the agents’ opinions on several topics. Unlike the existing models, these topics are interdependent, and hence the opinions being formed on these topics are also mutually dependent. We rigorously examine stability properties of the proposed model, in particular, convergence of the agents’ opinions. Although our model assumes synchronous communication among the agents, we show that the same final opinions may be reached “on average” via asynchronous gossip-based protocols.
Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/450bf4e997bc0cdc01e24ebaa11eb55a4b8198a5
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In Computer Networks and Communications
In Statistical and Nonlinear Physics
Citations in the corpus
- A tutorial on modeling and analysis of dynamic social networks. Part I (2017, methodology): “Any substochastic matrix A is either Schur stable ρ(A) < 1 or contains a stochastic submatrix [72]; if A is irreducible then it is either stochastic or Schur stable [70, 72].”