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.

CiliaGraph 是一种增强的表达性和超轻量级的 HDC（Hyper-Dimensional Computing）模型，用于图分类，通过引入一种新的节点编码策略，使用节点距离作为边权进行信息聚合，并将编码后的节点属性和结构信息进行拼接来获得综合的图表示，同时通过探索正交性与维度之间的关系来降低维度，从而提高计算效率，相比于目前最先进的 GNNs，CiliaGraph 能够显著减少内存使用和加速训练速度，而同时保持可比的准确性。

CiliaGraph：在边缘进行超轻量级和一次性图分类中加强表达的超维计算

CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation  in Ultra-Lightweight and One-Shot Graph Classification on Edge

We present a novel, simple, fast, and efficient approach for semi-supervised
learning on graphs. The proposed approach takes advantage of hyper-dimensional
computing which encodes data samples using random projections into a high
dimensional space (HD space for short). Specifically, we propose a
Hyper-dimensional Graph Learning (HDGL) algorithm that leverages the
injectivity property of the node representations of a family of graph neural
networks. HDGL maps node features to the HD space and then uses HD operators
such as bundling and binding to aggregate information from the local
neighborhood of each node. Results of experiments with widely used benchmark
data sets show that HDGL achieves predictive performance that is competitive
with the state-of-the-art deep learning methods, without the need for
computationally expensive training.

我们提出了一种新颖、简单、快速且高效的用于图像半监督学习的方法，该方法利用超高维度计算将数据样本编码成高维空间，并且通过 Hyper-dimensional Graph Learning 算法可以在图神经网络的节点表示上利用这种超高维度性质来实现信息聚合，从而取得与深度学习方法相媲美的预测性能，而无需进行计算昂贵的训练过程。