Adversarial Perturbations of Opinion Dynamics in Networks

Gaitonde, Jason; Kleinberg, Jon; Tardos, Éva

doi:10.1145/3391403.3399490

Cited by 42 publications

(29 citation statements)

References 11 publications

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“…NNIM provides a stochastic variant of known coevolutionary opinion formation models, thus generalizing existing deterministic ones. At the conceptual level, our work follows a significant and prolific research direction investigating the dynamics and the social and algorithmic efficiency of natural learning and opinion formation processes in social networks [1,13,14,26,44]. Building on previous work, we show how to efficiently exploit the results of such social learning processes for user interest prediction in OSN.…”

Section: Related Workmentioning

confidence: 94%

Stochastic Opinion Dynamics for Interest Prediction in Social Networks

Papachristou,

Fotakis

2021

Preprint

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We exploit the core-periphery structure and the strong homophilic properties of online social networks to develop faster and more accurate algorithms for user interest prediction. The core of modern social networks consists of relatively few influential users, whose interest profiles are publicly available, while the majority of peripheral users follow enough of them based on common interests. Our approach is to predict the interests of the peripheral nodes starting from the interests of their influential connections. To this end, we need a formal model that explains how common interests lead to network connections. Thus, we propose a stochastic interest formation model, the Nearest Neighbor Influence Model (NNIM), which is inspired by the Hegselmann-Krause opinion formation model and aims to explain how homophily shapes the network. Based on NNIM, we develop an efficient approach for predicting the interests of the peripheral users. At the technical level, we use Variational Expectation-Maximization to optimize the instantaneous likelihood function using a mean-field approximation of NNIM. We prove that our algorithm converges fast and is capable of scaling smoothly to networks with millions of nodes. Our experiments on standard network benchmarks demonstrate that our algorithm runs up to two orders of magnitude faster than the best known node embedding methods and achieves similar accuracy. CCS CONCEPTS• Information systems → Social networks.

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Section: Related Workmentioning

confidence: 94%

Stochastic Opinion Dynamics for Interest Prediction in Social Networks

Papachristou,

Fotakis

2021

Preprint

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“…From the definition, we can see that they are all quadratic forms of internal opinions s s s (or mean-centered express opinion z z z and meancentered internal opinion s s s). In addition, as shown in (Gaitonde et al, 2020), these three quadratic forms are all positive semi-definite, and thus, they are all convex functions with respect to s s s (or s s s and z z z).…”

Section: Quantifying Polarization and Disagreementmentioning

confidence: 98%

Evaluation and Control of Opinion Polarization and Disagreement: A Review

Li¹,

Zhao²

2021

Preprint

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With the recent advances of networking technology, connections among people are unprecedentedly enhanced. People with different ideologies and backgrounds interact with each other, and there may exist severe opinion polarization and disagreement in the social network. There have been a lot of reviews on modeling opinion formation. However, less attention has been paid to opinion polarization and disagreement.Design/methodology/approach: In this work, we review recent advances in opinion polarization and disagreement and pay attention to how they are evaluated and controlled.Findings: In literature, three metrics: polarization, disagreement, and polarizationdisagreement index (PDI) are usually adopted, and there is a tradeoff between polarization and disagreement. Different strategies have been proposed in literature which can significantly control opinion polarization and disagreement based on these metrics.Originality/value: This review is of crucial importance to summarize works on opinion polarization and disagreement, and to the better understanding and control of them.

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“…From the definition, we can see that they are all quadratic forms of internal opinions s (or mean-centered express opinion bold-italicztrue¯ and mean-centered internal opinion bold-italicstrue¯). In addition, as shown in Gaitonde et al (2020), these three quadratic forms are all positive semi-definite, and thus, they are all convex functions with respect to s (or bold-italicstrue¯ and bold-italicztrue¯).…”

Section: Evaluation and Analysis Of Polarization And Disagreementmentioning

confidence: 99%

Evaluation and control of opinion polarization and disagreement: a review

Zhao

2021

IJCS

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Purpose The purpose of this paper is to review the recent studies on opinion polarization and disagreement. Design/methodology/approach In this work, recent advances in opinion polarization and disagreement and pay attention to how they are evaluated and controlled are reviewed. Findings In literature, three metrics: polarization, disagreement and polarization-disagreement index are usually adopted and there is a tradeoff between polarization and disagreement. Different strategies have been proposed in literature which can significantly control opinion polarization and disagreement based on these metrics. Originality/value This review is of crucial importance to summarize works on opinion polarization and disagreement and to the better understanding and control of them.

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Adversarial Perturbations of Opinion Dynamics in Networks

Cited by 42 publications

References 11 publications

Stochastic Opinion Dynamics for Interest Prediction in Social Networks

Stochastic Opinion Dynamics for Interest Prediction in Social Networks

Evaluation and Control of Opinion Polarization and Disagreement: A Review

Evaluation and control of opinion polarization and disagreement: a review

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