Graph convolutional networks (GCNs) allow us to learn topologically-aware
node embeddings, which can be useful for classification or link prediction.
However, they are unable to capture long-range dependencies without adding
additional layers -- which in turn leads to over-smoothing and increased time
and space complexity. Further, the complex dependencies between nodes make
mini-batching challenging, limiting their applicability to large graphs. We
propose a Scalable Multi-resolution Graph Representation Learning (SMGRL)
framework that enables us to learn multi-resolution node embeddings
efficiently. Our framework is model-agnostic and can be applied to any existing
GCN model. We dramatically reduce training costs by training only on a
reduced-dimension coarsening of the original graph, then exploit
self-similarity to apply the resulting algorithm at multiple resolutions. The
resulting multi-resolution embeddings can be aggregated to yield high-quality
node embeddings that capture both long- and short-range dependencies. Our
experiments show that this leads to improved classification accuracy, without
incurring high computational costs.

我们提出了一个可应用于任何现有 GCN 模型的可扩展多分辨率图表示学习 (SMGRL) 框架，通过学习多分辨率节点嵌入有效地捕获长和短距离依赖，并将其聚合以产生捕获高质量节点嵌入的优化算法，同时提高分类准确度，而不会产生高计算成本。