# Evolutionary dynamics of higher-order interactions in social networks

arXiv: https://arxiv.org/abs/2001.10313 · HTML: https://mlsanity.com/paper/2001.10313/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2020.

## Built on by (most cited first, 26 of 26)

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- [Demystifying Higher-Order Graph Neural Networks](https://mlsanity.com/paper/2406.12841/) (2024)
- [Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactions](https://mlsanity.com/paper/2306.00904/) (2023)
- [Graph Retention Networks for Dynamic Graphs](https://mlsanity.com/paper/2411.11259/) (2024)
- [TAMI: Taming Heterogeneity in Temporal Interactions for Temporal Graph Link Prediction](https://mlsanity.com/paper/2510.23577/) (2025)
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- [Network Alignment](https://mlsanity.com/paper/2504.11367/) (2025)
- [Higher-Order Graph Databases](https://mlsanity.com/paper/2506.19661/) (2025)
- [TUC-PPO: Team Utility-Constrained Proximal Policy Optimization for Spatial Public Goods Games](https://mlsanity.com/paper/2507.02675/) (2025)
- [DCHO: A Decomposition-Composition Framework for Predicting Higher-Order Brain Connectivity to Enhance Diverse Downstream Applications](https://mlsanity.com/paper/2509.09696/) (2025)
- [Learning Multi-Order Block Structure in Higher-Order Networks](https://mlsanity.com/paper/2511.21350/) (2025)
- [TFWaveFormer: Temporal-Frequency Collaborative Multi-level Wavelet Transformer for Dynamic Link Prediction](https://mlsanity.com/paper/2603.03963/) (2026)

## Abstract (arXiv)

We live and cooperate in networks. However, links in networks only allow for pairwise interactions, thus making the framework suitable for dyadic games, but not for games that are played in groups of more than two players. Here, we study the evolutionary dynamics of a public goods game in social systems with higher-order interactions. First, we show that the game on uniform hypergraphs corresponds to the replicator dynamics in the well-mixed limit, providing a formal theoretical foundation to study cooperation in networked groups. Secondly, we unveil how the presence of hubs and the coexistence of interactions in groups of different sizes affects the evolution of cooperation. Finally, we apply the proposed framework to extract the actual dependence of the synergy factor on the size of a group from real-world collaboration data in science and technology. Our work provides a way to implement informed actions to boost cooperation in social groups.