# On the Expressive Power of Graph Neural Networks

arXiv: https://arxiv.org/abs/2401.01626 · HTML: https://mlsanity.com/paper/2401.01626/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Builds on (most cited first, 18 of 18)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [Directional Message Passing for Molecular Graphs](https://mlsanity.com/paper/2003.03123/) (2020)
- [Invariant and Equivariant Graph Networks](https://mlsanity.com/paper/1812.09902/) (2018)
- [Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting](https://mlsanity.com/paper/2006.09252/) (2020)
- [Revisiting Graph Neural Networks: All We Have is Low-Pass Filters](https://mlsanity.com/paper/1905.09550/) (2019)
- [Can Graph Neural Networks Count Substructures?](https://mlsanity.com/paper/2002.04025/) (2020)
- [Generalization and Representational Limits of Graph Neural Networks](https://mlsanity.com/paper/2002.06157/) (2020)
- [CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters](https://mlsanity.com/paper/1705.07664/) (2017)
- [What graph neural networks cannot learn: depth vs width](https://mlsanity.com/paper/1907.03199/) (2019)
- [The Surprising Power of Graph Neural Networks with Random Node Initialization](https://mlsanity.com/paper/2010.01179/) (2020)
- [Universal Invariant and Equivariant Graph Neural Networks](https://mlsanity.com/paper/1905.04943/) (2019)
- [On the equivalence between graph isomorphism testing and function approximation with GNNs](https://mlsanity.com/paper/1905.12560/) (2019)
- [On the Universality of Invariant Networks](https://mlsanity.com/paper/1901.09342/) (2019)
- [Approximation Ratios of Graph Neural Networks for Combinatorial Problems](https://mlsanity.com/paper/1905.10261/) (2019)
- [Mathematics of Deep Learning](https://mlsanity.com/paper/1712.04741/) (2017)

## Abstract (arXiv)

The study of Graph Neural Networks has received considerable interest in the past few years. By extending deep learning to graph-structured data, GNNs can solve a diverse set of tasks in fields including social science, chemistry, and medicine. The development of GNN architectures has largely been focused on improving empirical performance on tasks like node or graph classification. However, a line of recent work has instead sought to find GNN architectures that have desirable theoretical properties - by studying their expressive power and designing architectures that maximize this expressiveness. While there is no consensus on the best way to define the expressiveness of a GNN, it can be viewed from several well-motivated perspectives. Perhaps the most natural approach is to study the universal approximation properties of GNNs, much in the way that this has been studied extensively for MLPs. Another direction focuses on the extent to which GNNs can distinguish between different graph structures, relating this to the graph isomorphism test. Besides, a GNN's ability to compute graph properties such as graph moments has been suggested as another form of expressiveness. All of these different definitions are complementary and have yielded different recommendations for GNN architecture choices. In this paper, we would like to give an overview of the notion of "expressive power" of GNNs and provide some valuable insights regarding the design choices of GNNs.