Graph Neural Networks (GNNs) are computationally demanding and inefficient when applied to graph classification tasks in resource-constrained edge scenarios due to their inherent process, involving multiple rounds of forward and backward propagation. As a lightweight alternative, Hyper-Dimensional Computing (HDC), which leverages high-dimensional vectors for data encoding and processing, offers a more efficient solution by addressing computational bottleneck. However, current HDC methods primarily focus on static graphs and neglect to effectively capture node attributes and structural information, which leads to poor accuracy. In this work, we propose CiliaGraph, an enhanced expressive yet ultra-lightweight HDC model for graph classification. This model introduces a novel node encoding strategy that preserves relative distance isomorphism for accurate node connection representation. In addition, node distances are utilized as edge weights for information aggregation, and the encoded node attributes and structural information are concatenated to obtain a comprehensive graph representation. Furthermore, we explore the relationship between orthogonality and dimensionality to reduce the dimensions, thereby further enhancing computational efficiency. Compared to the SOTA GNNs, extensive experiments show that CiliaGraph reduces memory usage and accelerates training speed by an average of 292 times(up to 2341 times) and 103 times(up to 313 times) respectively while maintaining comparable accuracy.

翻译：图神经网络（GNNs）在图分类任务中，由于其固有的前向与反向传播多轮迭代过程，在资源受限的边缘场景下计算需求高且效率低下。作为一种轻量级替代方案，超维计算（HDC）利用高维向量进行数据编码与处理，通过解决计算瓶颈提供了更高效的解决方案。然而，当前的HDC方法主要关注静态图，未能有效捕捉节点属性与结构信息，导致分类精度不佳。本文提出CiliaGraph，一种用于图分类的、表达力增强且超轻量级的HDC模型。该模型引入了一种新颖的节点编码策略，通过保持相对距离同构性来精确表示节点连接关系。此外，模型利用节点距离作为边权重进行信息聚合，并将编码后的节点属性与结构信息拼接，以获得全面的图表示。进一步地，我们探索了正交性与维度之间的关系以降低维度，从而进一步提升计算效率。与最先进的GNNs相比，大量实验表明，CiliaGraph在保持相当精度的同时，平均分别将内存使用降低292倍（最高达2341倍）并将训练速度提升103倍（最高达313倍）。