Graph hyperdimensional computing (HDC) has emerged as a promising paradigm for cognitive tasks, emulating brain-like computation with high-dimensional vectors known as hypervectors. While HDC offers robustness and efficiency on graph-structured data, its fairness implications remain largely unexplored. In this paper, we study fairness in graph HDC, where biases in data representation and decision rules can lead to unequal treatment of different groups. We show how hypervector encoding and similarity-based classification can propagate or even amplify such biases, and we propose a fairness-aware training framework, FairGHDC, to mitigate them. FairGHDC introduces a bias correction term, derived from a gap-based demographic-parity regularizer, and converts it into a scalar fairness factor that scales the update of the class hypervector for the ground-truth label. This enables debiasing directly in the hypervector space without modifying the graph encoder or requiring backpropagation. Experimental results on six benchmark datasets demonstrate that FairGHDC substantially reduces demographic-parity and equal-opportunity gaps while maintaining accuracy comparable to standard GNNs and fairness-aware GNNs. At the same time, FairGHDC preserves the computational advantages of HDC, achieving up to about one order of magnitude ($\approx 10\times$) speedup in training time on GPU compared to GNN and fairness-aware GNN baselines.

翻译：图超维计算（HDC）作为一种模拟类脑计算的认知任务范式，通过使用称为超向量的高维向量，展现出广阔的应用前景。尽管HDC在图结构数据上具备鲁棒性和高效性，但其公平性影响仍鲜有研究。本文探讨图HDC中的公平性问题，其中数据表示和决策规则中的偏差可能导致对不同群体的不平等对待。我们展示了超向量编码和基于相似度的分类如何传播甚至放大此类偏差，并提出了一种公平感知的训练框架FairGHDC以缓解这些问题。FairGHDC引入了一个基于差距的人口均等正则化器推导出的偏差校正项，并将其转换为标量公平因子，用于缩放真实标签类别超向量的更新。这使得在超向量空间中直接进行去偏差成为可能，而无需修改图编码器或依赖反向传播。在六个基准数据集上的实验结果表明，FairGHDC显著减少了人口均等和机会均等差距，同时保持了与标准图神经网络（GNN）及公平感知GNN相当的准确性。此外，FairGHDC保留了HDC的计算优势，在GPU训练时间上相比GNN和公平感知GNN基线实现了约一个数量级（$\approx 10\times$）的加速。