Reviews: Diffusion Improves Graph Learning

This paper introduces graph diffusion convolution (GDC), a pre-processing pipeline for graphs useful in node classification experiments on homophilic network datasets (e.g., citation networks). The preprocessing pipeline consists of two steps: 1) replacing the original adjacency matrix with a diffusion matrix (obtained as a polynomial function of the original adjacency matrix), and 2) sparsification by, e.g., thresholding the values on the edges. When using the newly obtained adjacency matrix in typical graph learning frameworks, results on node classification improve in many cases. The paper is extremely well written and scores very high in terms of clarity and quality of exposition. Related work is covered in great detail.