[R] A PyTorch implementation of “MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing” (ICML 2019)
![[R] A PyTorch implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019)](https://b.thumbs.redditmedia.com/Ra7i8WsCcgzfP-V6XnsFxwsWHku_UGWLzYVLfjxNtbA.jpg "[R] A PyTorch implementation of \"MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing\" (ICML 2019)") |
https://i.redd.it/pjpxbslugjw21.jpg 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. submitted by /u/benitorosenberg |
