# Evolutionary games on multilayer networks: A colloquium

arXiv: https://arxiv.org/abs/1504.04359 · HTML: https://mlsanity.com/paper/1504.04359/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2015.

## Built on by (most cited first, 6 of 6)

- [mGNN: Generalizing the Graph Neural Networks to the Multilayer Case](https://mlsanity.com/paper/2109.10119/) (2021)
- [A Quantum-like Model for Predicting Human Decisions in the Entangled Social Systems](https://mlsanity.com/paper/2111.13902/) (2021)
- [Dynamic Evolution of Complex Networks: A Reinforcement Learning Approach Applying Evolutionary Games to Community Structure](https://mlsanity.com/paper/2506.17925/) (2025)
- [The Lost Art of Mathematical Modelling](https://mlsanity.com/paper/2301.08559/) (2023)
- [Learning the Right Layers: a Data-Driven Layer-Aggregation Strategy for Semi-Supervised Learning on Multilayer Graphs](https://mlsanity.com/paper/2306.00152/) (2023)
- [Network Alignment](https://mlsanity.com/paper/2504.11367/) (2025)

## Abstract (arXiv)

Networks form the backbone of many complex systems, ranging from the Internet to human societies. Accordingly, not only is the range of our interactions limited and thus best described and modeled by networks, it is also a fact that the networks that are an integral part of such models are often interdependent or even interconnected. Networks of networks or multilayer networks are therefore a more apt description of social systems. This colloquium is devoted to evolutionary games on multilayer networks, and in particular to the evolution of cooperation as one of the main pillars of modern human societies. We first give an overview of the most significant conceptual differences between single-layer and multilayer networks, and we provide basic definitions and a classification of the most commonly used terms. Subsequently, we review fascinating and counterintuitive evolutionary outcomes that emerge due to different types of interdependencies between otherwise independent populations. The focus is on coupling through the utilities of players, through the flow of information, as well as through the popularity of different strategies on different network layers. The colloquium highlights the importance of pattern formation and collective behavior for the promotion of cooperation under adverse conditions, as well as the synergies between network science and evolutionary game theory.