Qian Li

TL;DR: This work analytically derives λ(1)(A+αB) using a perturbation approximation for small and large α, the lower and upper bound for any α as a function of the adjacency matrix of the two individual networks, and the interconnections between the two and their largest eigenvalues and eigenvectors.

TL;DR: Interestingly, it is found that selecting the inflexible contrarians based on the leverage, the betweenness, or the degree is more effective in opinion-competition than using other centrality metrics in all types of networks.

TL;DR: In this paper, the correlation between centrality metrics in terms of their Pearson correlation coefficient and their similarity in ranking of nodes was studied. And the effect of inflexible contrarians selected based on different centrality measures in helping one opinion to compete with another in the inflexibility contrarian opinion (ICO) model was investigated.

TL;DR: For the NCO model on coupled networks, interactions through interdependent links could push the non-consensus opinion model to a consensus opinion model, which mimics the reality that increased mass communication causes people to hold opinions that are increasingly similar.

TL;DR: It is found that even for an Erdös-Rényi type model, where the degrees of the nodes are not so distinct, strategy II is significantly more effective in reducing the size of the largest A opinion cluster and, at very small values of p, the largest B opinion cluster is destroyed.