https://preview.redd.it/0kizekvf6lp31.jpg?width=1158&format=pjpg&auto=webp&s=070ab1a9035ae4a1808ddba2c5883ba138d658b1

GitHub: https://github.com/benedekrozemberczki/MixHop-and-N-GCN

Paper: https://arxiv.org/pdf/1905.00067.pdf

Abstract:

Recent methods generalize convolutional layers from Euclidean domains to graph-structured data by approximating the eigenbasis of the graph Laplacian. The computationally-efficient and broadly-used Graph ConvNet of Kipf & Welling, over-simplifies the approximation, effectively rendering graph convolution as a neighborhood-averaging operator. This simplification restricts the model from learning delta operators, the very premise of the graph Laplacian. In this work, we propose a new Graph Convolutional layer which mixes multiple powers of the adjacency matrix, allowing it to learn delta operators. Our layer exhibits the same memory footprint and computational complexity as a GCN. We illustrate the strength of our proposed layer on both synthetic graph datasets, and on several real-world citation graphs, setting the record state-of-the-art on Pubmed.