  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.
