부산대학교 도서관

Graph Neural Networks (GNNs) are increasingly being recognized as an effective method for learning representations from graph-structured data. Despite their potential, the substantial computational and energy demands of current deep learning-based methods limit their practical applicability in real-world scenarios. HyperDimensional Computing (HDC) offers a promising alternative, which is inspired by neuroscience. HDC leverages characteristics inherent in biological neural systems, such as high-dimensionality, randomness, and holographic representations, striking a balance between accuracy, efficiency, and robustness. In this paper, we introduce HyperNode, a cutting-edge node classification framework leveraging HDC for hardware-friendly computation. HyperNode encodes node features and edges using high-dimensional vectors in line with HDC principles. It establishes an HDC reference library by combining node classes. This library is subsequently employed during the node classification process, which aids in similarity checks of encoded query vectors. Remarkably, our framework drastically curtails the computational demands typical of conventional GNNs, supporting highly-parallelizable computation. Experimental results demonstrate that HyperNode achieves an average speed-up of 990.8 x and a 7.53% accuracy improvement compared to GNN models with similar performance on widely-recognized graph learning benchmarks. The proposed framework offers a promising solution for efficient and effective graph-based machine learning tasks.