Scaling up graph homomorphism for classification via sampling

8 April 2021

Papers citing "Scaling up graph homomorphism for classification via sampling"

10 / 10 papers shown

Title
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs Weihua Hu Matthias Fey Hongyu Ren Maho Nakata Yuxiao Dong J. Leskovec AI4CE 72 413 0 17 Mar 2021
TUDataset: A collection of benchmark datasets for learning with graphs Christopher Morris Nils M. Kriege Franka Bause Kristian Kersting Petra Mutzel Marion Neumann 236 821 0 16 Jul 2020
Graph Homomorphism Convolution Hoang NT Takanori Maehara 139 41 0 03 May 2020
Open Graph Benchmark: Datasets for Machine Learning on Graphs Weihua Hu Matthias Fey Marinka Zitnik Yuxiao Dong Hongyu Ren Bowen Liu Michele Catasta J. Leskovec 306 2,732 0 02 May 2020
Peregrine: A Pattern-Aware Graph Mining System Kasra Jamshidi Rakesh Mahadasa Keval Vora 45 94 0 06 Apr 2020
Benchmarking Graph Neural Networks Vijay Prakash Dwivedi Chaitanya K. Joshi Anh Tuan Luu T. Laurent Yoshua Bengio Xavier Bresson 438 940 0 02 Mar 2020
A Fair Comparison of Graph Neural Networks for Graph Classification Federico Errica Marco Podda D. Bacciu Alessio Micheli FaML 123 449 0 20 Dec 2019
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks Christopher Morris Martin Ritzert Matthias Fey William L. Hamilton J. E. Lenssen Gaurav Rattan Martin Grohe GNN 194 1,643 0 04 Oct 2018
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 810 3,291 0 24 Nov 2016
Distributed Representations of Words and Phrases and their Compositionality Tomas Mikolov Ilya Sutskever Kai Chen G. Corrado J. Dean NAI OCL 397 33,550 0 16 Oct 2013