arxiv_ai 95% Match Research Paper Researchers in GNNs and HDC,ML engineers focused on efficiency,Hardware designers for AI accelerators 1 week ago

HyperGraphX: Graph Transductive Learning with Hyperdimensional Computing and Message Passing

graph-neural-networks › graph-learning

📄 Abstract

Abstract: We present a novel algorithm, \hdgc, that marries graph convolution with binding and bundling operations in hyperdimensional computing for transductive graph learning. For prediction accuracy \hdgc outperforms major and popular graph neural network implementations as well as state-of-the-art hyperdimensional computing implementations for a collection of homophilic graphs and heterophilic graphs. Compared with the most accurate learning methodologies we have tested, on the same target GPU platform, \hdgc is on average 9561.0 and 144.5 times faster than \gcnii, a graph neural network implementation and HDGL, a hyperdimensional computing implementation, respectively. As the majority of the learning operates on binary vectors, we expect outstanding energy performance of \hdgc on neuromorphic and emerging process-in-memory devices.

Authors (4)

Guojing Cong

Tom Potok

Hamed Poursiami

Maryam Parsa

Submitted

October 28, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

Introduces \hdgc, a novel algorithm combining graph convolution with hyperdimensional computing (HDC) operations (binding and bundling) for transductive graph learning. This approach significantly outperforms GNNs and HDC methods in prediction accuracy and achieves dramatic speedups (9561x and 144.5x faster than GCNII and HDGL respectively), with expected outstanding energy performance on neuromorphic hardware due to binary vector operations.

Business Value

Enables faster and more energy-efficient analysis of large-scale graph data, unlocking new possibilities for real-time graph-based AI applications and reducing the cost of AI computation.

Paper Metadata

Innovation Type

Algorithmic/Methodological

Deployment Feasibility

Moderate. While the algorithm is efficient, widespread adoption requires hardware support (neuromorphic devices) or efficient software emulation.

Limitations Addressed

High computational cost and slow training/inference of traditional GNNs,Limited efficiency of existing HDC implementations for graph learning

Performance Gains

On average 9561.0 times faster than GCNII and 144.5 times faster than HDGL on the same target GPU platform, while achieving higher prediction accuracy.

Technical Tags

hyperdimensional computinggraph convolutionmessage passingtransductive learningbinding operationbundling operationbinary vectorsneuromorphic devicesenergy performance

Research Topics

Graph Neural NetworksHyperdimensional ComputingMachine Learning EfficiencyTransductive LearningHardware-Aware AI

Methods & Architectures

HDGC algorithm (marrying graph convolution with HDC)Binding and bundling operationsMessage passing Graph Convolutional Networks (GCNs)Hyperdimensional Computing Models

Applications & Tasks

Network Analysis Recommendation Systems Social Network Analysis Drug Discovery (potential) Scalability of GNNsComputational cost of GNNsEnergy consumption of ML models Transductive graph learningNode classificationGraph classification

Datasets & Benchmarks

Benchmarks

Homophilic graphs • Heterophilic graphs

Prediction accuracySpeed (inference time)

Related Fields

Graph Neural NetworksHyperdimensional ComputingMachine LearningComputer ArchitectureNeuromorphic Engineering

Keywords

graph neural networkshyperdimensional computingtransductive learningmessage passinggraph convolutionbindingbundlingenergy efficiencyneuromorphicfast inferencebinary vectors

Academic Context

#Graph Neural Networks#Hyperdimensional Computing#Machine Learning Efficiency#Transductive Learning#Hardware-Aware AI

Commercial Potential

Potential Products

Efficient graph analysis librariesHardware accelerators for graph processingReal-time recommendation engines

Target Industries

Social MediaE-commerceCybersecurityTelecommunicationsBiotechnology

Use Case Examples

Real-time fraud detection in financial transaction graphs.Accelerated analysis of protein-protein interaction networks.Fast community detection in large social networks.

Competitive Edge

Offers a significant leap in speed and energy efficiency for graph learning compared to traditional GNNs and existing HDC methods, while maintaining or improving accuracy.

Market Opportunity

Large market for graph analytics and efficient AI processing.

Revenue Models

Licensing of the algorithm/IPdevelopment of specialized hardwareconsulting services.

Resource Requirements

Compute Needs

Low computational requirements during inference, especially on neuromorphic hardware.

Data Requirements

Graph datasets (nodes, edges, features).

Deployment Constraints

Potential need for specialized hardware (neuromorphic) for maximum benefit; software emulation might incur overhead.

Scalability

Highly scalable due to the nature of HDC and binary vector operations.

Production Readiness

Maturity Level

Research

Time to Market

3-5 years for hardware integration, 1-2 years for software libraries.

Patent Potential

High, for the novel \hdgc algorithm and its integration principles.

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