Scaling Up Graph Neural Networks Via Graph Coarsening
Zengfeng Huang, Shengzhong Zhang, Chong Xi, Tang Liu, Min Qi Zhou · 2021
Scalability of graph neural networks remains one of the major challenges in graph machine learning. Since the representation of a node is computed by recursively aggregating and transforming representation vectors of its neighboring nodes from previous layers, the receptive fields grow exponentially, which makes standard stochastic optimization techniques ineffective. Various approaches have been proposed to alleviate this issue, e.g., sampling-based methods and techniques based on pre-computation of graph filters.