Molecular Classification Using Hyperdimensional Graph Classification
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916165733842944 |
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| author | Verges, Pere Nunes, Igor Heddes, Mike Givargis, Tony Nicolau, Alexandru |
| author_facet | Verges, Pere Nunes, Igor Heddes, Mike Givargis, Tony Nicolau, Alexandru |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12307 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Molecular Classification Using Hyperdimensional Graph Classification Verges, Pere Nunes, Igor Heddes, Mike Givargis, Tony Nicolau, Alexandru Machine Learning Artificial Intelligence Neural and Evolutionary Computing Quantitative Methods 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. |
| title | Molecular Classification Using Hyperdimensional Graph Classification |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing Quantitative Methods |
| url | https://arxiv.org/abs/2403.12307 |