Molecular Classification Using Hyperdimensional Graph Classification

# Molecular Classification Using Hyperdimensional Graph Classification

**arXiv ID:** 2403.12307
**Authors:** Pere Verges, Igor Nunes, Mike Heddes, Tony Givargis, Alexandru Nicolau
**Published:** 2024-03-18T23:16:17Z
**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.

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## References

- [arXiv:2403.12307](http://arxiv.org/abs/2403.12307v1)