Molecular Classification Using Hyperdimensional Graph Classification
Abstract
Our work introduces an innovative approach to graph learning by leveraging Hyperdimensional Computing. Graphs serve as a widely embraced method for conveying information, and their utilization in learning has gained significant attention. This is notable in the field of chemoinformatics, where learning from graph representations plays a pivotal role. An important application within this domain involves the identification of cancerous cells across diverse molecular structures. We propose an HDC-based model that demonstrates comparable Area Under the Curve results when compared to state-of-the-art models like Graph Neural Networks (GNNs) or the Weisfieler-Lehman graph kernel (WL). Moreover, it outperforms previously proposed hyperdimensional computing graph learning methods. Furthermore, it achieves noteworthy speed enhancements, boasting a 40x acceleration in the training phase and a 15x improvement in inference time compared to GNN and WL models. This not only underscores the efficacy of the HDC-based method, but also highlights its potential for expedited and resource-efficient graph learning.
Cite
@article{arxiv.2403.12307,
title = {Molecular Classification Using Hyperdimensional Graph Classification},
author = {Pere Verges and Igor Nunes and Mike Heddes and Tony Givargis and Alexandru Nicolau},
journal= {arXiv preprint arXiv:2403.12307},
year = {2024}
}