Hypergraphx Achieves 144.5times Faster Graph Transductive Learning with Hyperdimensional Computing and Message Passing

Graph-based machine learning increasingly powers applications from social network analysis to drug discovery, yet current methods often struggle with both accuracy and efficiency. Guojing Cong, Tom Potok, and Hamed Poursiami, alongside Maryam Parsa and colleagues at Oak Ridge National Laboratory and George Mason University, present a new approach called HyperGraphX that dramatically improves performance in this field. Their algorithm uniquely combines the strengths of graph convolution with hyperdimensional computing and message passing, achieving superior prediction accuracy on both common and challenging graph structures. Importantly, HyperGraphX delivers these results with exceptional speed, surpassing leading graph neural networks and hyperdimensional methods by a significant margin and promising substantial energy savings on future computing hardware.

The team demonstrates that HyperGraphX achieves superior accuracy compared to existing graph neural networks, graph attention networks, and other hyperdimensional learning implementations across a range of benchmark graphs. Notably, the algorithm performs particularly well on heterophilic graphs, which often pose challenges for many existing methods. Beyond improved accuracy, HyperGraphX exhibits substantial gains in runtime performance, significantly outperforming both traditional graph neural networks and alternative hyperdimensional approaches.

The results indicate that HyperGraphX is orders of magnitude faster than current state-of-the-art implementations. The authors acknowledge that the algorithm’s performance has been demonstrated on specific graph benchmarks and plan to explore its implementation on emerging neuromorphic devices. Future work will also investigate the application of HyperGraphX to graph classification tasks, potentially broadening its utility in machine learning applications.

Graph Learning via Convolution and Hyperdimensions

This paper introduces HyperGraphX, a novel approach to transductive learning that combines graph convolutional networks with hyperdimensional computing. The authors demonstrate that HyperGraphX achieves state-of-the-art performance in both accuracy and speed, particularly on challenging heterophilic graphs. Key contributions include a novel approach that leverages the strengths of both graph convolutional networks, for capturing graph structure, and hyperdimensional computing, for efficient representation and computation. HyperGraphX outperforms existing graph neural networks and hyperdimensional computing-based graph learning methods on several benchmark datasets, excelling on heterophilic graphs.

The method is significantly faster than all compared methods, achieving speedups of several orders of magnitude, and allows for efficient computation and representation of graph data. The authors highlight the potential of implementing HyperGraphX on neuromorphic hardware for further performance gains. HyperGraphX combines graph convolution to extract features from the graph structure with hyperdimensional computing to represent these features as high-dimensional vectors. These vectors are then used for classification or other downstream tasks, allowing for efficient similarity comparisons and learning. Improved accuracy, especially on challenging graph types, faster training and inference, scalability through the hyperdimensional computing representation, and potential for hardware acceleration make HyperGraphX a promising new approach.

HyperGraphX Achieves Fast Transductive Graph Learning

The research team presents HyperGraphX, a novel algorithm that combines graph convolution with binding and bundling operations for transductive graph learning. Experiments demonstrate that HyperGraphX outperforms major graph neural network implementations and state-of-the-art hyperdimensional implementations across a collection of both homophilic and heterophilic graphs. Specifically, on a standard GPU platform, HyperGraphX is, on average, 9561. 0 and 144. 5times faster than GCNII and HDGL, respectively.

The study evaluated performance on seven networks, including the citation networks Cora, Citeseer, and Pubmed, and the heterophilic networks Chameleon, Cornell, Texas, and Wisconsin. With limited training data, only 20 labeled nodes per class, HyperGraphX achieves approximately 15. 5, 12. 0, and 1. 0 percentage point higher accuracy than GCN, GAT, and GCNII, respectively, on the homophilic graphs.

On the heterophilic graphs, HyperGraphX exhibits outstanding performance, achieving accuracy gains of 29. 8, 24. 3, 17. 4, 12. 2, 17.

6, and 5. 8 percentage points over GCN, GAT, Geom-GCN-I, Geom-GCN-P, Geom-GCN-S, and GCNII, respectively. Notably, HyperGraphX achieves 0. 844 accuracy on the Wisconsin dataset, surpassing GCNII, which utilizes a 16-layer network, by approximately 10 percentage points. The team also measured training times, revealing that HyperGraphX completes training in 0.

0046, 0. 0130, and 0. 0102 seconds for Cora, Citeseer, and Pubmed, respectively, significantly faster than all other implementations tested. These results demonstrate the efficiency and effectiveness of HyperGraphX for transductive graph learning, particularly in scenarios with limited training data and complex graph structures.