# Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks

arXiv: https://arxiv.org/abs/2010.05313 · HTML: https://mlsanity.com/paper/2010.05313/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 30 of 30)

- [A Review of Graph Neural Networks in Epidemic Modeling](https://mlsanity.com/paper/2403.19852/) (2024)
- [Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications](https://mlsanity.com/paper/2206.07922/) (2022)
- [Reinforcement learning on graphs: A survey](https://mlsanity.com/paper/2204.06127/) (2022)
- [Efficient Subgraph GNNs by Learning Effective Selection Policies](https://mlsanity.com/paper/2310.20082/) (2023)
- [Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective](https://mlsanity.com/paper/2404.06492/) (2024)
- [A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization](https://mlsanity.com/paper/2211.03074/) (2022)
- [Socially Fair Reinforcement Learning](https://mlsanity.com/paper/2208.12584/) (2022)
- [Temporal Multiresolution Graph Neural Networks For Epidemic Prediction](https://mlsanity.com/paper/2205.14831/) (2022)
- [Cooperating Graph Neural Networks with Deep Reinforcement Learning for Vaccine Prioritization](https://mlsanity.com/paper/2305.05163/) (2023)
- [Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey](https://mlsanity.com/paper/2202.09363/) (2022)
- [Road Planning for Slums via Deep Reinforcement Learning](https://mlsanity.com/paper/2305.13060/) (2023)
- [PGODE: Towards High-quality System Dynamics Modeling](https://mlsanity.com/paper/2311.06554/) (2023)
- [A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems](https://mlsanity.com/paper/2109.12073/) (2021)
- [Optimizing Tensor Network Contraction Using Reinforcement Learning](https://mlsanity.com/paper/2204.09052/) (2022)
- [A Primer on Temporal Graph Learning](https://mlsanity.com/paper/2401.03988/) (2024)
- [Reasoning over Multi-view Knowledge Graphs](https://mlsanity.com/paper/2209.13702/) (2022)
- [Graph Learning and Its Advancements on Large Language Models: A Holistic Survey](https://mlsanity.com/paper/2212.08966/) (2022)
- [A Direct Approximation of AIXI Using Logical State Abstractions](https://mlsanity.com/paper/2210.06917/) (2022)
- [Containing a spread through sequential learning: to exploit or to explore?](https://mlsanity.com/paper/2303.00141/) (2023)
- [Joint Combinatorial Node Selection and Resource Allocations in the Lightning Network using Attention-based Reinforcement Learning](https://mlsanity.com/paper/2411.17353/) (2024)
- [Modelling the Recommender Alignment Problem](https://mlsanity.com/paper/2208.12299/) (2022)
- [Amortized Network Intervention to Steer the Excitatory Point Processes](https://mlsanity.com/paper/2310.04159/) (2023)
- [Application of deep and reinforcement learning to boundary control problems](https://mlsanity.com/paper/2310.15191/) (2023)
- [Dynamically Local-Enhancement Planner for Large-Scale Autonomous Driving](https://mlsanity.com/paper/2502.21134/) (2025)
- [Influence Maximization in Temporal Social Networks with a Cold-Start Problem: A Supervised Approach](https://mlsanity.com/paper/2504.11245/) (2025)
- [Metric spaces of walks and Lipschitz duality on graphs](https://mlsanity.com/paper/2508.19709/) (2025)
- [Vulnerable Agent Identification in Large-Scale Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2509.15103/) (2025)
- [Dynamic Treatment on Networks](https://mlsanity.com/paper/2605.06564/) (2026)
- [TANGCO: Learning Topology-Aware Capacity Allocation for Overload-driven Cascading Failures](https://mlsanity.com/paper/2608.13212/) (2026)
- [Beyond Static Graph World Models: Learning Stochastic Latent Dynamics over Evolving Topologies](https://mlsanity.com/paper/2609.28670/) (2026)

## Abstract (arXiv)

We consider the problem of controlling a partially-observed dynamic process on a graph by a limited number of interventions. This problem naturally arises in contexts such as scheduling virus tests to curb an epidemic; targeted marketing in order to promote a product; and manually inspecting posts to detect fake news spreading on social networks. We formulate this setup as a sequential decision problem over a temporal graph process. In face of an exponential state space, combinatorial action space and partial observability, we design a novel tractable scheme to control dynamical processes on temporal graphs. We successfully apply our approach to two popular problems that fall into our framework: prioritizing which nodes should be tested in order to curb the spread of an epidemic, and influence maximization on a graph.