mlsanityData to 7 Oct 2026

A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part I

arXiv 1701.06307 ↗First posted 23 Jan 2017eess.SY cs.MA cs.SIcited by 15

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Abstract (arXiv)

In recent years, we have observed a significant trend towards filling the gap between social network analysis and control. This trend was enabled by the introduction of new mathematical models describing dynamics of social groups, the advancement in complex networks theory and multi-agent systems, and the development of modern computational tools for big data analysis. The aim of this tutorial is to highlight a novel chapter of control theory, dealing with applications to social systems, to the attention of the broad research community. This paper is the first part of the tutorial, and it is focused on the most classical models of social dynamics and on their relations to the recent achievements in multi-agent systems.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jan 2017.

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Citing papers per year: 2022: 1; 2023: 4; 2024: 0; 2025: 3; 2026: 7.

15 papers.

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  1. 2022ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networkscited by 31
  2. 2023Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalizationcited by 26
  3. 2025Decision-Dependent Stochastic Optimization: The Role of Distribution Dynamicscited by 5
  4. 2026Hidden Anchors in Multi-Agent LLM Deliberationcited by 2
  5. 2026Social Networks of LLM Agentscited by 2
  6. 2023A Unified View on Neural Message Passing with Opinion Dynamics for Social Networkscited by 1
  7. 2026AI-Mediated Communication Can Steer Collective Opinioncited by 1
  8. 2023On the convergence of nonlinear averaging dynamics with three-body interactions on hypergraphscited by 0
  9. 2023Joint Learning of Network Topology and Opinion Dynamics Based on Bandit Algorithmscited by 0
  10. 2025Personalized and Resilient Distributed Learning Through Opinion Dynamicscited by 0
  11. 2025Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitionscited by 0
  12. 2026Socially-Aware Recommender Systems Mitigate Opinion Clusterizationcited by 0
3 more
  1. 2026SURGE: An Event-Centric Social Media Sentiment Time Series Benchmark with Interaction Structurecited by 0
  2. 2026Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?cited by 0
  3. 2026Digital Twins for Opinion Dynamics: A Generative LLM Framework for Social Networkscited by 0