# Theoretically Expressive and Edge-aware Graph Learning

arXiv: https://arxiv.org/abs/2001.09005 · HTML: https://mlsanity.com/paper/2001.09005/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [A Generalized Weisfeiler-Lehman Graph Kernel](https://mlsanity.com/paper/2101.08104/) (2021)
- [Bayesian Deep Learning for Graphs](https://mlsanity.com/paper/2202.12348/) (2022)
- [Enhancing Deep Learning Models through Tensorization: A Comprehensive Survey and Framework](https://mlsanity.com/paper/2309.02428/) (2023)

## Abstract (arXiv)

We propose a new Graph Neural Network that combines recent advancements in the field. We give theoretical contributions by proving that the model is strictly more general than the Graph Isomorphism Network and the Gated Graph Neural Network, as it can approximate the same functions and deal with arbitrary edge values. Then, we show how a single node information can flow through the graph unchanged.