The Research On Complex Networks Opinion Dynamics

The present age is the era of the Internet. The technology in this age develops so quickly that it has linked all of us so closely. If each body in it can be seen as a node, then the Internet itself can be seen as a giant network. Maybe we can call it the complex networks,because its inner structure is so complicated. It is not have been long for the researchers to study complex networks, but in recent years it has received widely attention again from many researchers as the world's population is growing and the connection between people is more closer. The research object of complex networks can be anything which exist in any form on the planet. As an inter-discipline, the complex networks has been researched by many scholars, many of them are even devoting all their lives to it. As the era of big data is coming, we can see that the complex networks has existed in the real life in each aspect. So the research on it can make a big difference in people's lives.The ultimate purpose for the researchers to study one subject is that they hope they can apply their research production to the real life. And so it is with us. We hope in the research process on the complex networks, we can combine the related theories and the final production with human society, and make a solution to the difficulty which we human faces, and promote the society's development at the same time. In this paper, at first, we have studied the opinion dynamics in community networks, then we have studied multiple layered opinion dynamics under heterogeneous confidence, at last we have studied the convergence and consensus of the opinion dynamics in the complex networks. Our works can be summarized as follows:(1) Opinion dynamics in community networks. The community network has many fantastic properties, on the basis of the classical HK model, simultaneously combining the characteristics of the community network, we have proposed our algorithm. If two agents are in the same community, and they are linking with each other, then we can say that they can be neighbors with each other. If two agents are in the different communities, and they are linking with each other, and the distance between their opinions is less than a certain value, then we can also say that they can be neighbors with each other. When one agents chooses its neighbors according to our algorithm, then it must be updating its own opinions by some certain ways. Based on this algorithm, we have observed the opinion evolution of opinion dynamics. At the same time, in the community network, we also consider another situation: without taking the community structure into consideration, we continue observing the evolution process of the opinion dynamics, and make a comparison between the two situations.(2) The multiple layered opinion dynamics with heterogeneous bounded confidence.Given a group with a specific size N, then each agent in the group has been assigned a random value which was distributed in the interval 0~10. Then we split the whole group into three sections, and the proportion of each section is variable. Of course, each agent in the group should be assigned a bounded confidence which obeys the power law distribution. Through changing the proportions of the three sections, we observe the final cluster numbers and the maximum cluster size of the group, and also its evolution process.(3) The consensus of opinion dynamics in complex networks. In the traditional HK model,when the bounded confidence is small, then the whole network wouldn't be ending up with consensus but polarization in most cases. In other words, all the agents in the group do not share the same opinion value. Based on the HK model, we have improved the algorithm and put forward our own views. Generally speaking, on the research of consensus about opinion dynamics, there has fewer scholars do the research on the real complex networks.In this paper, we have studied the consensus about the opinion dynamics in the real complex networks such as BA scale free network, WS small world network and the ER stochastic network. In simple terms, at first, we choose the first part of the neighbors according to the network's topology structure, that is to say the adjacency matrix. Then we choose the second part of the neighbors according to the basic HK model. Besides the above two, we also choose the third part of neighbors according to the probability rule.When one agent has chosen its neighbors, then it will update its opinion by averaging all its neighbors' opinion. The result show that, for a given smaller bounded confidence, the whole network can also reach consensus.