One-Shot Graph Representation Learning Using Hyperdimensional Computing
Abhishek Dalvi Vasant Honavar
We present a novel, simple, fast, and efficient approach for semi-supervised
learning on graphs. The proposed approach takes advantage of hyper-dimensional
computing which encodes data samples using random projections into a high
dimensional space (HD space for short). Specifically, we propose a
Hyper-dimensional Graph Learning (HDGL) algorithm that leverages the
injectivity property of the node representations of a family of graph neural
networks. HDGL maps node features to the HD space and then uses HD operators
such as bundling and binding to aggregate information from the local
neighborhood of each node. Results of experiments with widely used benchmark
data sets show that HDGL achieves predictive performance that is competitive
with the state-of-the-art deep learning methods, without the need for
computationally expensive training.