Voter model on adaptive networks

2021 ◽  
Author(s):  
Jinming Du
Keyword(s):  
Network Topology ◽  
Statistical Physics ◽  
Evolutionary Game ◽  
Dynamic Networks ◽  
Real Data ◽  
Voter Model ◽  
Adaptive Networks ◽  
Opinion Formation ◽  
Basic Model ◽  
Individual Behaviors

Abstract Voter model is an important basic model in statistical physics. In recent years, it has been more and more used to describe the process of opinion formation in sociophysics. In real complex systems, the interactive network of individuals is dynamically adjusted, and the evolving network topology and individual behaviors affect each other. Therefore, we propose a linking dynamics to describe the coevolution of network topology and individual behaviors in this paper, and study the voter model on the adaptive network. We theoretically analyze the properties of the voter model, including consensus probability and time. The evolution of opinions on dynamic networks is further analyzed from the perspective of evolutionary game. Finally, a case study of real data is shown to verify the effectiveness of the theory.

scholarly journals STUDIES OF OPINION STABILITY FOR SMALL DYNAMIC NETWORKS WITH OPPORTUNISTIC AGENTS

2009 ◽  
Vol 20 (10) ◽  
pp. 1645-1662 ◽  
Author(s):  
PAWEL SOBKOWICZ
Keyword(s):  
Social Network ◽  
Computer Model ◽  
Network Topology ◽  
Network Structure ◽  
Real World ◽  
Minority Groups ◽  
Dynamic Networks ◽  
Social Structures ◽  
Dynamical Model ◽  
Opinion Formation

There are numerous examples of societies with extremely stable mix of contrasting opinions. We argue that this stability is a result of an interplay between society network topology adjustment and opinion changing processes. To support this position we present a computer model of opinion formation based on some novel assumptions, designed to bring the model closer to social reality. In our model, the agents, in addition to changing their opinions due to influence of the rest of society and external propaganda, have the ability to modify their social network, forming links with agents sharing the same opinions and cutting the links with those they disagree with. To improve the model further we divide the agents into "fanatics" and "opportunists," depending on how easy it is to change their opinions. The simulations show significant differences compared to traditional models, where network links are static. In particular, for the dynamical model where inter-agent links are adjustable, the final network structure and opinion distribution is shown to resemble real world observations, such as social structures and persistence of minority groups even when most of the society is against them and the propaganda is strong.

Mechanism analysis of competitive information asynchronous dissemination on social networks

2019 ◽  
Vol 30 (11) ◽  
pp. 1950094 ◽  
Author(s):  
Jianye Yu ◽  
Junjie Lv ◽  
Yuanzhuo Wang ◽  
Jingyuan Li
Keyword(s):  
Social Networks ◽  
Information Diffusion ◽  
Information Dissemination ◽  
Evolutionary Game ◽  
Real Data ◽  
Game Model ◽  
Mechanism Analysis ◽  
Sina Weibo ◽  
Human Behaviors ◽  
Information Spread

Information dissemination groups, especially those disseminating the same kind of information such as advertising, product promotion, etc., compete with each other when their information spread on social networks. Most of the existing methods analyze the dissemination mechanism mainly upon the information itself without considering human characteristics, e.g. relation networks, cooperation/defection, etc. In this paper, we introduce a framework of social evolutionary game (SEG) to investigate the influence of human behaviors in competitive information dissemination. Coordination game is applied to represent human behaviors in the competition of asynchronous information diffusion. We perform a series of simulations through a specific game model to analyze the mechanism and factors of information diffusion, and show that when the benefits of competitive information is around 1.2 times of the original one, it can compensate the loss of reputation caused by the change of strategy. Furthermore, through experiments on a dataset of two films on Sina Weibo, we described the mechanism of competition evolution over real data of social network, and validated the effectiveness of our model.

scholarly journals COLLABORATIVE LEARNING BY BOOSTING IN DISTRIBUTED ENVIRONMENTS

2010 ◽  
Vol 24 (05) ◽  
pp. 763-789
Author(s):  
SHIJUN WANG ◽  
CHANGSHUI ZHANG
Keyword(s):  
Network Topology ◽  
Distributed Learning ◽  
Real Data ◽  
Distributed Applications ◽  
Data Sets ◽  
Human Society ◽  
Scale Free ◽  
Comparison Results ◽  
Algorithm Comparison ◽  
Training Error

In human society, people learn from each other and knowledge is accumulated from generation to generation. This provides some hints to distributed learning. For distributed applications, each site has its own data. If we can build a local model for each site and improve the model based on models learned by its neighbor sites with low communication cost, then it would be very helpful to the distributed applications. In this paper, we propose a new distributed learning method called distributed network boosting (DNB) algorithm for distributed applications. The learned hypotheses are exchanged between neighboring sites during learning process. Theoretical analysis shows that the DNB algorithm minimizes the cost function through collaborative functional gradient descent in hypotheses space. We also give upper bounds of training error and generalization error of the DNB algorithm. Comparison results of the DNB algorithm with other algorithms on real data sets with different sizes show the effectiveness of the proposed algorithm for distributed applications. In order to show the influence of network topology on the performance of the DNB algorithm, we tested it on random graphs and scale-free networks. Bias-variance decomposition shows that the network topology plays an important role in controlling the diversity of the learned classifier ensemble.

scholarly journals The (T, L)-Path Model and Algorithms for Information Dissemination in Dynamic Networks

Information ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 212
Author(s):  
Zhiwei Yang ◽  
Weigang Wu
Keyword(s):  
Network Topology ◽  
Information Dissemination ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Network Models ◽  
Point Of View ◽  
Path Model ◽  
Dynamic Network Models ◽  
Topology Changes ◽  
Practical Design

A dynamic network is the abstraction of distributed systems with frequent network topology changes. With such dynamic network models, fundamental distributed computing problems can be formally studied with rigorous correctness. Although quite a number of models have been proposed and studied for dynamic networks, the existing models are usually defined from the point of view of connectivity properties. In this paper, instead, we examine the dynamicity of network topology according to the procedure of changes, i.e., how the topology or links change. Following such an approach, we propose the notion of the “instant path” and define two dynamic network models based on the instant path. Based on these two models, we design distributed algorithms for the problem of information dissemination respectively, one of the fundamental distributing computing problems. The correctness of our algorithms is formally proved and their performance in time cost and communication cost is analyzed. Compared with existing connectivity based dynamic network models and algorithms, our procedure based ones are definitely easier to be instantiated in the practical design and deployment of dynamic networks.

DYNAMICS WITH CO-EVOLUTION OF INDIVIDUAL INCLINATION AND OPINION

2011 ◽  
Vol 22 (01) ◽  
pp. 51-62 ◽  
Author(s):  
FEI XIONG ◽  
YUN LIU ◽  
ZHENJIANG ZHANG
Keyword(s):  
Opinion Dynamics ◽  
Spin Systems ◽  
Voter Model ◽  
Formation Model ◽  
Opinion Formation ◽  
Final State ◽  
Memory Length ◽  
Slowing Down ◽  
The Past ◽  
The Individual

Based on the voter model, we present a new opinion formation model which takes into account the evolution of both opinions and individual inclinations. A memory-based inclination is developed gradually during the process of social interaction; however, if the individual inclination gets strong enough, it will react to opinion dynamics. We assume that an individual inclination increases with the number of times the individual has held its most frequent opinion in the past interactions. As a result of inclination choices the transition rate following neighbors decreases, thus slowing down the microscopic dynamics. Analytical and simulation results indicate the system under the action of opinion inclinations evolves to a more polarized state for average opinion. The appearance of extremists holding the minority opinion is observed in the final state, where one opinion predominates. It is also found that the stable opinion and relaxation time depend on network topology and memory length. Moreover, this model is not only valid to the voter model, but can also be applied to other spin systems.

scholarly journals COLLECTIVE POLITICAL OPINION FORMATION IN NONLINEAR SOCIAL INTERACTION

2007 ◽  
Vol 18 (09) ◽  
pp. 1429-1434 ◽  
Author(s):  
SOO YONG KIM ◽  
CHUNG HYUN PARK ◽  
KYUNGSIK KIM
Keyword(s):  
Statistical Physics ◽  
Party Systems ◽  
Military Coup ◽  
Opinion Formation ◽  
Collective Behaviors ◽  
Political Dynamics ◽  
Political Unrest ◽  
Decision Making Processes ◽  
Independent Decision ◽  
Political Opinions

We have presented a numerical model of a collective opinion formation procedure to explain political phenomena such as two-party and multi-party systems in politics, political unrest, military coup d'etats and netizen revolutions. Nonlinear interaction with binary and independent decision making processes can yield various collective behaviors or collective political opinions. Statistical physics and nonlinear dynamics may provide useful tools to study various socio-political dynamics.

scholarly journals Incremental Density-Based Link Clustering Algorithm for Community Detection in Dynamic Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Fanrong Meng ◽  
Feng Zhang ◽  
Mu Zhu ◽  
Yan Xing ◽  
Zhixiao Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Topology ◽  
Clustering Algorithm ◽  
Dynamic Networks ◽  
Experimental Results ◽  
Extended Version ◽  
High Quality ◽  
Detection Algorithms

Community detection in complex networks has become a research hotspot in recent years. However, most of the existing community detection algorithms are designed for the static networks; namely, the connections between the nodes are invariable. In this paper, we propose an incremental density-based link clustering algorithm for community detection in dynamic networks, iDBLINK. This algorithm is an extended version of DBLINK which is proposed in our previous work. It can update the local link community structure in the current moment through the change of similarity between the edges at the adjacent moments, which includes the creation, growth, merging, deletion, contraction, and division of link communities. Extensive experimental results demonstrate that iDBLINK not only has a great time efficiency, but also maintains a high quality community detection performance when the network topology is changing.

scholarly journals A Dynamic Evolutionary Game Model of Collaborative Innovation in Manufacturing Services Industry and Manufacturing Industry

2021 ◽  
Vol 15 ◽  
pp. 1099-1108
Author(s):  
Deguang Liu
Keyword(s):  
Value Chain ◽  
Manufacturing Industry ◽  
Evolutionary Game ◽  
Spillover Effect ◽  
Transfer Of Knowledge ◽  
Producer Services ◽  
Basic Model ◽  
Collaborative Innovation ◽  
Innovation Model ◽  
The One

Collaborative innovation has a significant impact on the efficiency of manufacturing services and manufacturing innovation. In this paper, a collaborative innovation model of manufacturing services and manufacturing is constructed based on the two-dimensional asymmetric evolutionary game basic model. The stable evolution strategy of the model is to be found through the solutions to the replicator dynamic differential equation of both sides of the game. The results show that on the one hand, producer services can rely on the carrier of knowledge capital and human capital to link to the manufacturing process from front to back, and form the forward and backward spillover effect. On the other hand, the knowledge elements in producer services, especially tacit knowledge, are transmitted through the modern network under the common industrial culture atmosphere in the process of continuous industrial interaction and industrial integration, which can promote the sharing and transfer of knowledge, produce interactive innovation, and finally promote the innovation of value chain.

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