The paper addresses an overlooked phenomenon in opinion dynamics research: the clustering of identical opinions on network topology. The paper investigates the distribution and clustering of steady-state opinions on networks through the probability distribution function of the proportion of neighbors holding different opinion around a specific type of opinion. Simulations of the Deffuant-Weisbuch model′s opinion diffusion process reveal two key characteristics of steady-state opinion distributions in ER random graphs converging into two opinion clusters: the probability distribution function follows a binomial distribution, and identical opinion aggregation induces a probability bias, where the product of the bias and the network′s average degree remains constant. Building upon these findings, this study extends the results to small-world networks and examines how clustering coefficient influences the steady-state distribution and clustering of opinions, thereby further validating and broadening the general applicability of the research outcomes.
SHAN Zhoujun
,
PENG Shiyu
,
CHEN Xi
. Steady-state Opinion: Distribution and Clustering on Networks[J]. Complex Systems and Complexity Science, 2026
, 23(4)
: 27
-34
.
DOI: 10.13306/j.1672-3813.2026.04.004
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