scholarly journals Influential Node Control Strategy for Opinion Evolution on Social Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Cheng Ju ◽  
Jinde Cao ◽  
Weiqi Zhang ◽  
Mengxin Ji
Keyword(s):  
Social Networks ◽  
Control Strategy ◽  
Small World ◽  
Opinion Dynamics ◽  
Node Degree ◽  
New Model ◽  
New Strategy ◽  
Influential Nodes ◽  
Basic Features ◽  
Opinion Evolution

We study opinion dynamics in social networks and present a new strategy to control the invasive opinion. A developed continuous-opinion evolution model is proposed to describe the mechanism of making decision in closed community. Two basic strategies of evolution are determined, and some basic features of our new model are analyzed. We study the different invasive strategies. It is shown via using Monte Carlo simulations that our new model shows different invulnerability with traditional model. Node degree and cohesion in invasive small-world community plays less significant role when the evolution of opinion is continuous rather than dichotomous. Using simulation, we find one kind of Influential Nodes that can affect the outcome dramatically, while these Influential Nodes are sensitive to their node degree and the evolution weight. Thus, we develop invasive control strategy based on these features.

scholarly journals Adaptive bridge control strategy for opinion evolution on social networks

2011 ◽  
Vol 21 (2) ◽  
pp. 025116 ◽  
Author(s):  
Cheng Qian ◽  
Jinde Cao ◽  
Jianquan Lu ◽  
Jürgen Kurths
Keyword(s):  
Social Networks ◽  
Control Strategy ◽  
Opinion Evolution

COMPARING EFFECTS OF CLUSTER-COUPLED PATTERNS ON OPINION DYNAMICS

2012 ◽  
Vol 23 (07) ◽  
pp. 1250050 ◽  
Author(s):  
YUN LIU ◽  
XIA-MENG SI ◽  
YAN-CHAO ZHANG
Keyword(s):  
Community Structure ◽  
Social Cohesion ◽  
Small World ◽  
Opinion Dynamics ◽  
Scale Free ◽  
Update Rules ◽  
Opinion Evolution ◽  
Multiple Cluster ◽  
Better Than ◽  
Continuous Opinions

Community structure is another important feature besides small-world and scale-free property of complex networks. Communities can be coupled through specific fixed links between nodes, or occasional encounter behavior. We introduce a model for opinion evolution with multiple cluster-coupled patterns, in which the interconnectivity denotes the coupled degree of communities by fixed links, and encounter frequency controls the coupled degree of communities by encounter behaviors. Considering the complicated cognitive system of people, the CODA (continuous opinions and discrete actions) update rules are used to mimic how people update their decisions after interacting with someone. It is shown that, large interconnectivity and encounter frequency both can promote consensus, reduce competition between communities and propagate some opinion successfully across the whole population. Encounter frequency is better than interconnectivity at facilitating the consensus of decisions. When the degree of social cohesion is same, small interconnectivity has better effects on lessening the competence between communities than small encounter frequency does, while large encounter frequency can make the greater degree of agreement across the whole populations than large interconnectivity can.

scholarly journals Extracting Influential Nodes for Maximization Influence in Social Networks

2021 ◽  
Vol 1818 (1) ◽  
pp. 012177
Author(s):  
Zainab Naseem Attuah ◽  
Firas Sabar Miften ◽  
Evan Abdulkareem Huzan
Keyword(s):  
Social Networks ◽  
Influential Nodes

PHYSICS OF THE MIND: OPINION DYNAMICS AND DECISION MAKING PROCESSES BASED ON A BINARY NETWORK MODEL

2008 ◽  
Vol 22 (25n26) ◽  
pp. 4482-4494 ◽  
Author(s):  
F. V. KUSMARTSEV ◽  
KARL E. KÜRTEN
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Chaotic Behavior ◽  
Opinion Dynamics ◽  
Human Mind ◽  
Critical Parameters ◽  
Opinion Formation ◽  
Natural Catastrophes ◽  
Risk Condition ◽  
Binary Network

We propose a new theory of the human mind. The formation of human mind is considered as a collective process of the mutual interaction of people via exchange of opinions and formation of collective decisions. We investigate the associated dynamical processes of the decision making when people are put in different conditions including risk situations in natural catastrophes when the decision must be made very fast or at national elections. We also investigate conditions at which the fast formation of opinion is arising as a result of open discussions or public vote. Under a risk condition the system is very close to chaos and therefore the opinion formation is related to the order disorder transition. We study dramatic changes which may happen with societies which in physical terms may be considered as phase transitions from ordered to chaotic behavior. Our results are applicable to changes which are arising in various social networks as well as in opinion formation arising as a result of open discussions. One focus of this study is the determination of critical parameters, which influence a formation of stable mind, public opinion and where the society is placed “at the edge of chaos”. We show that social networks have both, the necessary stability and the potential for evolutionary improvements or self-destruction. We also show that the time needed for a discussion to take a proper decision depends crucially on the nature of the interactions between the entities as well as on the topology of the social networks.

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