We present a novel algorithm, \hdgc, that marries graph convolution with binding and bundling operations in hyperdimensional computing for transductive graph learning. For prediction accuracy \hdgc outperforms major and popular graph neural network implementations as well as state-of-the-art hyperdimensional computing implementations for a collection of homophilic graphs and heterophilic graphs. Compared with the most accurate learning methodologies we have tested, on the same target GPU platform, \hdgc is on average 9561.0 and 144.5 times faster than \gcnii, a graph neural network implementation and HDGL, a hyperdimensional computing implementation, respectively. As the majority of the learning operates on binary vectors, we expect outstanding energy performance of \hdgc on neuromorphic and emerging process-in-memory devices.

翻译：我们提出了一种新颖算法——超维图传导（HDGC），该算法将图卷积与超维计算中的绑定和捆绑操作相结合，用于传导式图学习。在预测准确性方面，HDGC在一系列同配图和异配图上超越了主流且流行的图神经网络实现以及最先进的超维计算实现。与我们测试过的最准确学习方法相比，在同一目标GPU平台上，HDGC平均比图神经网络实现GCNII快9561.0倍，比超维计算实现HDGL快144.5倍。由于大部分学习过程在二值向量上运行，我们预期HDGC在神经形态计算和新兴存内处理设备上将展现出卓越的能效表现。