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

arXiv: https://arxiv.org/abs/1701.06307 · HTML: https://mlsanity.com/paper/1701.06307/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2017.

## Built on by (most cited first, 15 of 15)

- [ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks](https://mlsanity.com/paper/2206.05437/) (2022)
- [Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization](https://mlsanity.com/paper/2311.11114/) (2023)
- [Decision-Dependent Stochastic Optimization: The Role of Distribution Dynamics](https://mlsanity.com/paper/2503.07324/) (2025)
- [Hidden Anchors in Multi-Agent LLM Deliberation](https://mlsanity.com/paper/2606.19494/) (2026)
- [Social Networks of LLM Agents](https://mlsanity.com/paper/2607.03695/) (2026)
- [A Unified View on Neural Message Passing with Opinion Dynamics for Social Networks](https://mlsanity.com/paper/2310.01272/) (2023)
- [AI-Mediated Communication Can Steer Collective Opinion](https://mlsanity.com/paper/2605.16245/) (2026)
- [On the convergence of nonlinear averaging dynamics with three-body interactions on hypergraphs](https://mlsanity.com/paper/2304.07203/) (2023)
- [Joint Learning of Network Topology and Opinion Dynamics Based on Bandit Algorithms](https://mlsanity.com/paper/2306.15695/) (2023)
- [Personalized and Resilient Distributed Learning Through Opinion Dynamics](https://mlsanity.com/paper/2505.14081/) (2025)
- [Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions](https://mlsanity.com/paper/2506.02897/) (2025)
- [Socially-Aware Recommender Systems Mitigate Opinion Clusterization](https://mlsanity.com/paper/2601.02412/) (2026)
- [SURGE: An Event-Centric Social Media Sentiment Time Series Benchmark with Interaction Structure](https://mlsanity.com/paper/2605.21198/) (2026)
- [Multi-Agent Systems are Mixtures of Experts: Who Becomes an Influencer?](https://mlsanity.com/paper/2605.25929/) (2026)
- [Digital Twins for Opinion Dynamics: A Generative LLM Framework for Social Networks](https://mlsanity.com/paper/2609.19913/) (2026)

## 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.