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.

翻译：我们的工作引入了一种利用超维计算进行图学习的创新方法。图作为一种广泛采用的信息传递方式，其在学习中的应用已获得显著关注，尤其在化学信息学领域——基于图表示的学习发挥着关键作用。该领域的一个重要应用涉及跨不同分子结构的癌细胞识别。我们提出的基于HDC模型在曲线下面积指标上展现出与图神经网络或Weisfeiler-Lehman图核等现有最优模型相当的性能，同时优于先前提出的超维计算图学习方法。此外，该模型实现了显著的速度提升：相较于图神经网络和WL模型，训练阶段加速40倍，推理时间提升15倍。这不仅证明了基于HDC方法的有效性，更凸显了其在实现快速、资源高效的图学习方面的潜力。