# GraphHD: Efficient graph classification using hyperdimensional computing arXiv: https://arxiv.org/abs/2205.07826 · HTML: https://mlsanity.com/paper/2205.07826/ ## Key facts - Cited by 28 papers in the map (data through 14 Oct 2026). - Builds on 9 of its 44 references that are in the map. - First posted on arXiv 16 May 2022. ## Builds on (most cited first, 9 of 9) - [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018) - [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019) - [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018) - [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020) - [A Survey on Graph Kernels](https://mlsanity.com/paper/1903.11835/) (2019) - [PageRank beyond the Web](https://mlsanity.com/paper/1407.5107/) (2014) - [On Valid Optimal Assignment Kernels and Applications to Graph Classification](https://mlsanity.com/paper/1606.01141/) (2016) - [The Multiscale Laplacian Graph Kernel](https://mlsanity.com/paper/1603.06186/) (2016) - [Hardware Optimizations of Dense Binary Hyperdimensional Computing: Rematerialization of Hypervectors, Binarized Bundling, and Combinational Associative Memory](https://mlsanity.com/paper/1807.08583/) (2018) ## Built on by (most cited first, 28 of 28) - [Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures](https://mlsanity.com/paper/2205.09208/) (2022) - [HyperSense: Hyperdimensional Intelligent Sensing for Energy-Efficient Sparse Data Processing](https://mlsanity.com/paper/2401.10267/) (2024) - [NeuroHash: A Hyperdimensional Neuro-Symbolic Framework for Spatially-Aware Image Hashing and Retrieval](https://mlsanity.com/paper/2404.11025/) (2024) - [Generalized Holographic Reduced Representations](https://mlsanity.com/paper/2405.09689/) (2024) - [Hyperdimensional Uncertainty Quantification for Multimodal Uncertainty Fusion in Autonomous Vehicles Perception](https://mlsanity.com/paper/2503.20011/) (2025) - [Hyperdimensional Representation Learning for Node Classification and Link Prediction](https://mlsanity.com/paper/2402.17073/) (2024) - [HDCC: A Hyperdimensional Computing compiler for classification on embedded systems and high-performance computing](https://mlsanity.com/paper/2304.12398/) (2023) - [Resource-Efficient Federated Hyperdimensional Computing](https://mlsanity.com/paper/2306.01339/) (2023) - [HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning](https://mlsanity.com/paper/2403.05763/) (2024) - [Hyperdimensional Intelligent Sensing for Efficient Real-Time Audio Processing on Extreme Edge](https://mlsanity.com/paper/2502.10718/) (2025) - [Graph Embeddings via Tensor Products and Approximately Orthonormal Codes](https://mlsanity.com/paper/2208.10917/) (2022) - [The Hyperdimensional Transform for Distributional Modelling, Regression and Classification](https://mlsanity.com/paper/2311.08150/) (2023) - [Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data](https://mlsanity.com/paper/2402.17572/) (2024) - [Self-Attention Based Semantic Decomposition in Vector Symbolic Architectures](https://mlsanity.com/paper/2403.13218/) (2024) - [DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing](https://mlsanity.com/paper/2505.05413/) (2025) - [Bridging the Gap Between Hyperdimensional Computing and Kernel Methods via the Nystr"om Method](https://mlsanity.com/paper/2608.06860/) (2026) - [Molecular Classification Using Hyperdimensional Graph Classification](https://mlsanity.com/paper/2403.12307/) (2024) - [VS-Graph: Scalable and Efficient Graph Classification Using Hyperdimensional Computing](https://mlsanity.com/paper/2512.03394/) (2025) - [Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective](https://mlsanity.com/paper/2603.03830/) (2026) - [Random Projections of Sparse Adjacency Matrices](https://mlsanity.com/paper/2309.01360/) (2023) - [Towards Efficient Hyperdimensional Computing Using Photonics](https://mlsanity.com/paper/2311.17801/) (2023) - [CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation in Ultra-Lightweight and One-Shot Graph Classification on Edge](https://mlsanity.com/paper/2405.19033/) (2024) - [Hyperdimensional Quantum Factorization](https://mlsanity.com/paper/2406.11889/) (2024) - [A Vector Symbolic Approach to Multiple Instance Learning](https://mlsanity.com/paper/2511.16795/) (2025) - [Mitigating Bias in Graph Hyperdimensional Computing](https://mlsanity.com/paper/2512.07433/) (2025) - [ConformalHDC: Uncertainty-Aware Hyperdimensional Computing with Application to Neural Decoding](https://mlsanity.com/paper/2602.21446/) (2026) - [QeHDC: Hyperdimensional Computing based on Quantum-enhanced binding and SuperClass Construction](https://mlsanity.com/paper/2606.22421/) (2026) - [Compiler-Driven Approximation Tuning for Hyperdimensional Computing](https://mlsanity.com/paper/2606.26547/) (2026) ## Abstract (arXiv) Hyperdimensional Computing (HDC) developed by Kanerva is a computational model for machine learning inspired by neuroscience. HDC exploits characteristics of biological neural systems such as high-dimensionality, randomness and a holographic representation of information to achieve a good balance between accuracy, efficiency and robustness. HDC models have already been proven to be useful in different learning applications, especially in resource-limited settings such as the increasingly popular Internet of Things (IoT). One class of learning tasks that is missing from the current body of work on HDC is graph classification. Graphs are among the most important forms of information representation, yet, to this day, HDC algorithms have not been applied to the graph learning problem in a general sense. Moreover, graph learning in IoT and sensor networks, with limited compute capabilities, introduce challenges to the overall design methodology. In this paper, we present GraphHD-a baseline approach for graph classification with HDC. We evaluate GraphHD on real-world graph classification problems. Our results show that when compared to the state-of-the-art Graph Neural Networks (GNNs) the proposed model achieves comparable accuracy, while training and inference times are on average 14.6× and 2.0× faster, respectively.