Consensus In Social Networks Research Articles

Emergence mechanisms of group consensus in social networks

Emergence mechanisms of group consensus in social networks

Consensus progress for large-scale group decision making in social networks with incomplete probabilistic hesitant fuzzy information

Decision-making situations have been dramatically impacted by the development of the economy and technology. Large-scale group decision making (LSGDM) in social networks has become one of the hot topics in the decision-making field. In this paper, we develop a novel framework to reach consensus in social networks for LSGDM with incomplete probabilistic hesitant fuzzy information. In this framework, an expert clustering method based on interpretive structural modeling (ISM) is designed to classify experts. The trust propagation and aggregation operators for probabilistic hesitant fuzzy are explored to achieve indirect trust assessment and weights of experts. Then, an iterative algorithm that checks the personal decision level of experts is presented to estimate the missing values in incomplete probabilistic hesitant fuzzy decision matrices. Also, a consensus process is investigated to eliminate opinion conflict using trust feedback adjustment (TFA) and weight feedback adjustment (WFA). Lastly, a case study is presented to demonstrate the application of the proposed method. The comparison analysis and discussion are established to verify the advantages and effectiveness of the proposed consensus framework for LSGDM in social networks.

Sure Event Problem in Multicomponent Dynamical Systems with Attractive Interaction

We establish a series of sufficient conditions for the realization of a sure event as the limit (in time) state of a multicomponent dynamical system with attractive interaction. The sure event is characterized by the state of a system with finitely many positions when all coordinates of the distribution are equal to zero, except a single fixed coordinate equal to 1. The sure event can be interpreted as the state of consensus in social networks and, hence, the obtained results can be used in voter and opinion-formation models.

Consensus in social networks: Revisited

We analyze the convergence of opinions or beliefs in a general social network with non-Bayesian agents. We provide a new sufficient condition under which opinions converge to consensus and the condition is significantly more permissive than that of Lorenz (2005). This condition, which depends on properties of the network, requires agents to incorporate others’ opinions into their own posterior sufficiently often.

A Novel Interpretation for Opinion Consensus in Social Networks With Antagonisms

We take a new perspective for the consensus in DeGroot-type social networks with antagonistic interactions between some pairs of agents. We observe the analogies between social networks and electrical networks. A line with positive (or negative) conductance in the electrical network well corresponds to the cooperative (or antagonistic) interaction in the social network. Then, we introduce a refined definition of effective conductance (EC), which comes from electrical networks, into social networks as a characterization of the overall relationship between a pair of agents. The EC considers the effects of both direct and indirect interactions between the agents. Some EC-based consensus criteria are established by analytical and statistical approaches, showing that the sign of EC is a useful indicator of consensus. The opinion consensus can be generally interpreted as every pair of agents being overall cooperative despite antagonistic interactions, i.e., the corresponding EC being positive. The obtained results provide new insights into the consensus mechanism with clear intuition. Case study of a 15-agent network is provided as an illustration.

Opinion dynamics and consensus in social networks

Opinion dynamics and consensus in social networks

Opinion dynamics in social networks with stubborn agents: An issue-based perspective

Classic models on opinion dynamics usually focus on a group of agents forming their opinions interactively over single issue. Yet generally consensus can not be achieved over single issue when agents are not completely open to interpersonal influence. In this paper, opinion consensus in social networks with stubborn agents is considered over issue sequences. The social network with stubborn agents is described by the Friedkin-Johnsen (F-J) model where agents are stubborn to their initial opinions. Firstly, we propose some sufficient and necessary conditions both in terms of network topology and system matrix for convergence of the F-J model over single issue. Secondly, opinion consensus of the F-J model is investigated over issue sequences. Our analysis establishes connections between the interpersonal influence network and the network describing the relationship of agents' initial opinions for successive issues. Taking advantage of these connections, we derive the sufficient and necessary condition for the F-J model to achieve opinion consensus over issue sequences. Finally, we consider a more general scenario where each agent has bounded confidence in forming its initial opinion for every issue. By analyzing the evolution of agents' ultimate opinions for each issue over issue sequences, we prove that the connectivity of the state-dependent network is preserved in this setting. Then the conditions for agents to achieve opinion consensus over issue sequences are proposed. Simulation examples are provided to illustrate the effectiveness of our theoretical results.

Consensus in Social Networks of Compound Nodes

Opinion dynamics in network structures of special type are considered. Each node consists of two agents interacting with each other. The properties of consensus arising in such structures are studied using the DeGroot model.

Reaching Consensus in Social Networks

This paper considers network based non-Bayesian opinion formation on a linearly ordered set of opinions. The general class of constricting and continuous Markov revision functions, that contains the standard weighted average revision functions, is analyzed. A revision function is constricting if the revised opinion is strictly higher (lower) ranked than the lowest (highest) ranked observed opinion. The main advantages of the general approach are that (i) it captures a wide range of applications, and (ii) the constricting property is easily testable. It is shown that asymptotic consensus occurs in strongly connected networks whenever the revision functions of all agents are constricting and continuous. The revision function does not need to be the same across agents, or across time for a given agent. Additionally, asymptotic consensus is shown to hold almost surely if agents are subject to a natural class of probabilistic mistakes when forming their opinions.

Robust consensus in social networks and coalitional games

We study an n-player averaging process with dynamics subject to controls and adversarial disturbances. The model arises in two distinct application domains: i) coalitional games with transferable utilities (TU) and ii) opinion propagation. We study conditions under which the average allocations achieve robust consensus to some predefined target set.

Reaching Consensus in Social Networks

This paper considers network based non-Bayesian opinion formation on a linearly ordered set of opinions. The general class of constricting and continuous Markov revision functions, that contains the standard weighted average revision functions, is analyzed. A revision function is constricting if the revised opinion is strictly higher (lower) ranked than the lowest (highest) ranked observed opinion. The main advantages of the general approach are that (i) it captures a wide range of applications, and (ii) the constricting property is easily testable.It is shown that asymptotic consensus occurs in strongly connected networks whenever the revision functions of all agents are constricting and continuous. The revision function does not need to be the same across agents, or across time for a given agent. Additionally, asymptotic consensus is shown to hold almost surely if agents are subject to a natural class of probabilistic mistakes when forming their opinions.