# Expressive Graph Informer Networks

arXiv: https://arxiv.org/abs/1907.11318 · HTML: https://mlsanity.com/paper/1907.11318/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Collaborative Drug Discovery: Inference-level Data Protection Perspective](https://mlsanity.com/paper/2205.06506/) (2022)

## Abstract (arXiv)

Applying machine learning to molecules is challenging because of their natural representation as graphs rather than vectors.Several architectures have been recently proposed for deep learning from molecular graphs, but they suffer from informationbottlenecks because they only pass information from a graph node to its direct neighbors. Here, we introduce a more expressiveroute-based multi-attention mechanism that incorporates features from routes between node pairs. We call the resulting methodGraph Informer. A single network layer can therefore attend to nodes several steps away. We show empirically that the proposedmethod compares favorably against existing approaches in two prediction tasks: (1) 13C Nuclear Magnetic Resonance (NMR)spectra, improving the state-of-the-art with an MAE of 1.35 ppm and (2) predicting drug bioactivity and toxicity. Additionally, wedevelop a variant called injective Graph Informer that isprovablyas powerful as the Weisfeiler-Lehman test for graph isomorphism.Furthermore, we demonstrate that the route information allows the method to be informed about thenonlocal topologyof the graphand, thus, even go beyond the capabilities of the Weisfeiler-Lehman test.