# Predicting Steady-State Behavior in Complex Networks with Graph Neural Networks

arXiv: https://arxiv.org/abs/2502.01693 · HTML: https://mlsanity.com/paper/2502.01693/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 2 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020)
- [Understanding Isomorphism Bias in Graph Data Sets](https://mlsanity.com/paper/1910.12091/) (2019)
- [Successful strategies for competing networks](https://mlsanity.com/paper/1303.0183/) (2013)
- [Robust Spectral Detection of Global Structures in the Data by Learning a Regularization](https://mlsanity.com/paper/1609.02906/) (2016)
- [Network construction: A learning framework through localizing principal eigenvector](https://mlsanity.com/paper/1802.00202/) (2018)
- [Multilevel Digital Contact Tracing](https://mlsanity.com/paper/2007.05637/) (2020)
- [Derivation of Back-propagation for Graph Convolutional Networks using Matrix Calculus and its Application to Explainable Artificial Intelligence](https://mlsanity.com/paper/2408.01408/) (2024)

## Abstract (arXiv)

In complex systems, information propagation can be defined as diffused or delocalized, weakly localized, and strongly localized. This study investigates the application of graph neural network models to learn the behavior of a linear dynamical system on networks. A graph convolution and attention-based neural network framework has been developed to identify the steady-state behavior of the linear dynamical system. We reveal that our trained model distinguishes the different states with high accuracy. Furthermore, we have evaluated model performance with real-world data. In addition, to understand the explainability of our model, we provide an analytical derivation for the forward and backward propagation of our framework.