Hyperdimensional Computing for Node Classification and Link Prediction

• URL: http://arxiv.org/abs/2402.17073v2
• Date: Sat, 20 Jul 2024 03:46:13 GMT
• Title: Hyperdimensional Computing for Node Classification and Link Prediction
• Authors: Abhishek Dalvi, Vasant Honavar,
• Abstract summary: We introduce a novel method for transductive learning on graphs using hyperdimensional representations. The proposed approach encodes data samples using random projections into a very high-dimensional space. It obviates the need for expensive iterative training of the sort required by deep learning methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a novel method for transductive learning on graphs using hyperdimensional representations. The proposed approach encodes data samples using random projections into a very high-dimensional space (hyperdimensional or HD space for short). It obviates the need for expensive iterative training of the sort required by deep learning methods. Specifically, we propose a Hyperdimensional Graph Learning (HDGL) algorithm. HDGL leverages the \emph{injectivity} property of node representations of a family of Graph Neural Networks (GNNs) to map 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. The resulting latent node representations support both node classification and link prediction tasks, unlike typical deep learning methods, which often require separate models for these tasks. We report results of experiments using widely used benchmark datasets which demonstrate that, on the node classification task, HDGL is competitive with the SOTA GNN methods with respect to accuracy, at substantially reduced computational cost. Furthermore, HDGL is well-suited for class incremental learning where the model has to learn to effectively discriminate between a growing number of classes. Our experiments also show that the HD representation constructed by HDGL supports link prediction at accuracies comparable to that of DeepWalk and related methods, although it falls short of SOTA Graph Neural Network (GNN) methods that rely on computationally expensive iterative training. We conclude that HDGL offers a computationally efficient alternative to graph neural networks for node classification, especially in settings that call for class-incremental learning or in applications that demand high accuracy models at significantly lower computational cost and learning time than possible with the SOTA GNNs.

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