ﻻ يوجد ملخص باللغة العربية
In Hopfield neural networks with up to 10^8 nodes we store two patterns through Hebb couplings. Then we start with a third random pattern which is supposed to evolve into one of the two stored patterns, simulating the cognitive process of associative memory leading to one of two possible opinions. With probability p each neuron independently, instead of following the Hopfield rule, takes over the corresponding value of another network, thus simulating how different people can convince each other. A consensus is achieved for high p.
In this work we study the opinion evolution in a community-based population with intergroup interactions. We address two issues. First, we consider that such intergroup interactions can be negative with some probability $p$. We develop a coupled mean
Here we developed a new conceptual, stochastic Heterogeneous Opinion-Status model (HOpS model), which is adaptive network model. The HOpS model admits to identify the main attributes of dynamics on networks and to study analytically the relation betw
In some social networks, the opinion forming is based on its own and neighbors (initial) opinions, whereas the evolution of the individual opinions is also influenced by the individuals past opinions in the real world. Unlike existing social network
Opinion dynamics concerns social processes through which populations or groups of individuals agree or disagree on specific issues. As such, modelling opinion dynamics represents an important research area that has been progressively acquiring releva
In this work, we investigate a heterogeneous population in the modified Hegselmann-Krause opinion model on complex networks. We introduce the Shannon information entropy about all relative opinion clusters to characterize the cluster profile in the f