Distributed randomized algorithms for opinion formation, centrality computation and power systems estimation: A tutorial overview

Frasca, Paolo; Ishii, Hideaki; Ravazzi, Chiara; Tempo, Roberto

doi:10.1016/j.ejcon.2015.04.002

Cited by 41 publications

(40 citation statements)

References 75 publications

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“…A similar behavior is demonstrated by the randomized algorithms for PageRank computation [207][208][209], whose relation to the asynchronous gossip-based FJ model has been disclosed in [200,201].…”

Section: The Asynchronous Gossip-based Fj Modelmentioning

confidence: 54%

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A tutorial on modeling and analysis of dynamic social networks. Part II

Proskurnikov

Tempo

2018

Annual Reviews in Control

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255

135

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Recent years have witnessed a significant trend towards filling the gap between Social Network Analysis (SNA) and control theory. This trend was enabled by the introduction of new mathematical models describing dynamics of social groups, the development of algorithms and software for data analysis and the tremendous progress in understanding complex networks and multi-agent systems (MAS) dynamics. The aim of this tutorial is to highlight a novel chapter of control theory, dealing with dynamic models of social networks and processes over them, to the attention of the broad research community. In its first part [1], we have considered the most classical models of social dynamics, which have anticipated and to a great extent inspired the recent extensive studies on MAS and complex networks. This paper is the second part of the tutorial, and it is focused on more recent models of social processes that have been developed concurrently with MAS theory. Future perspectives of control in social and techno-social systems are also discussed.touched by control theory, despite the long-term studies on social group dynamics [24][25][26] and "sociocybernetics" [27][28][29][30]. This gap between SNA and control can be explained, to a great extent, by the lack of dynamic models of social processes and mathematical armamentarium for their analysis. Focusing on topological properties of networks, SNA and network science have paid much less attention to dynamics over them, except for some special processes such as e.g. random walks, branching and queueing processes, percolation and contagion dynamics [19,20].The recent years have witnessed an important tendency towards filling the gap between SNA and control theory, enabled by the rapid progress in multiagent systems and dynamic networks. The emerging branch of control theory, studying social processes, is very young and even has no name yet. However, the interest of sociologists to this new area and understanding that "coordination and control of social systems is the foundational problem of sociology" [31] leaves no doubt that it should become a key instrument to examine social networks and dynamics over them. Without aiming to provide a exhaustive survey of "social control theory" at its dawn, this tutorial fo-

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Section: The Asynchronous Gossip-based Fj Modelmentioning

confidence: 54%

“…At the same time, the distribution of the opinion vector x(k) converges to some probability measure. In this subsection, we discuss similar results 13 , concerning the asynchronous gossip-based version of the Friedkin-Johnsen (FJ) model [199][200][201][202][203].…”

Section: Gossiping With Stubborn Agentsmentioning

confidence: 56%

A tutorial on modeling and analysis of dynamic social networks. Part II

Proskurnikov

Tempo

2018

Annual Reviews in Control

Self Cite

255

135

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“…In the recent literature on distributed social learning, agents aim at estimating a common unobservable state from noisy observations through non-Bayesian schemes in which each agent processes its own and its neighbors' beliefs [10]- [14], see also [15] for a tutorial. A different batch of references investigates interpersonal influences in groups of individuals and the emerging of asymptotic opinions, [16]- [18], see [2], [19] for a tutorial on opinion formation in social networks. The problem of self-rating in a social environment is discussed in [20], where agents can perform a predefined task, but with different abilities.…”

Section: Introductionmentioning

confidence: 99%

Distributed Learning from Interactions in Social Networks

Sasso

Coluccia

Notarstefano

2018

2018 European Control Conference (ECC)

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We consider a network scenario in which agents can evaluate each other according to a score graph that models some interactions. The goal is to design a distributed protocol, run by the agents, that allows them to learn their unknown state among a finite set of possible values. We propose a Bayesian framework in which scores and states are associated to probabilistic events with unknown parameters and hyperparameters, respectively. We show that each agent can learn its state by means of a local Bayesian classifier and a (centralized) Maximum-Likelihood (ML) estimator of parameter-hyperparameter that combines plain ML and Empirical Bayes approaches. By using tools from graphical models, which allow us to gain insight on conditional dependencies of scores and states, we provide a relaxed probabilistic model that ultimately leads to a parameter-hyperparameter estimator amenable to distributed computation. To highlight the appropriateness of the proposed relaxation, we demonstrate the distributed estimators on a social interaction set-up for user profiling.

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“…At the same time, the FJ model has been confirmed by experiments with real social groups (Friedkin and Johnsen, 2011;Friedkin et al, 2016a). The FJ model is closely (Friedkin and Johnsen, 2014;Proskurnikov et al, 2016b) and has been given some elegant game-theoretic and electric interpretations (Bindel et al, 2011;Ghaderi and Srikant, 2014;Frasca et al, 2015). In the recent works (Parsegov et al, 2017;Proskurnikov and Tempo, 2017) necessary and sufficient conditions for the stability of the FJ model has been established; these conditions also provide convergence "on average" of its decentralized gossip-based counterpart (Frasca et al, 2013;Ravazzi et al, 2015;Frasca et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Opinion evolution in time-varying social influence networks with prejudiced agents

et al. 2017

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Investigation of social influence dynamics requires mathematical models that are "simple" enough to admit rigorous analysis, and yet sufficiently "rich" to capture salient features of social groups. Thus, the mechanism of iterative opinion pooling from (DeGroot, 1974), which can explain the generation of consensus, was elaborated in (Friedkin and Johnsen, 1999) to take into account individuals' ongoing attachments to their initial opinions, or prejudices. The "anchorage" of individuals to their prejudices may disable reaching consensus and cause disagreement in a social influence network. Further elaboration of this model may be achieved by relaxing its restrictive assumption of a time-invariant influence network. During opinion dynamics on an issue, arcs of interpersonal influence may be added or subtracted from the network, and the influence weights assigned by an individual to his/her neighbors may alter. In this paper, we establish new important properties of the (Friedkin and Johnsen, 1999) opinion formation model, and also examine its extension to time-varying social influence networks.

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Distributed randomized algorithms for opinion formation, centrality computation and power systems estimation: A tutorial overview

Cited by 41 publications

References 75 publications

A tutorial on modeling and analysis of dynamic social networks. Part II

A tutorial on modeling and analysis of dynamic social networks. Part II

Distributed Learning from Interactions in Social Networks

Opinion evolution in time-varying social influence networks with prejudiced agents

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