Molecular Classification Using Hyperdimensional Graph Classification
Pere Verges Igor Nunes Mike Heddes Tony Givargis Alexandru Nicolau
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.