no code implementations • 7 Dec 2023 • Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec
The core idea is to view relational databases as a temporal, heterogeneous graph, with a node for each row in each table, and edges specified by primary-foreign key links.
2 code implementations • NeurIPS 2023 • Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael Bronstein, Guillaume Rabusseau, Reihaneh Rabbany
We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs.
no code implementations • 18 Dec 2021 • Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten Borgwardt
In recent years, algorithms and neural architectures based on the Weisfeiler--Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful tool for machine learning with graphs and relational data.
1 code implementation • 10 Jun 2021 • Matthias Fey, Jan E. Lenssen, Frank Weichert, Jure Leskovec
We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs to large graphs.
no code implementations • 12 May 2021 • Christopher Morris, Matthias Fey, Nils M. Kriege
In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, emerged as a powerful tool for (supervised) machine learning with graphs and relational data.
6 code implementations • 17 Mar 2021 • Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, Jure Leskovec
Enabling effective and efficient machine learning (ML) over large-scale graph data (e. g., graphs with billions of edges) can have a great impact on both industrial and scientific applications.
Ranked #1 on Knowledge Graphs on WikiKG90M-LSC
1 code implementation • 22 Jun 2020 • Matthias Fey, Jan-Gin Yuen, Frank Weichert
We present a hierarchical neural message passing architecture for learning on molecular graphs.
Ranked #25 on Graph Property Prediction on ogbg-molhiv
20 code implementations • NeurIPS 2020 • Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec
We present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research.
Ranked #1 on Link Property Prediction on ogbl-citation2
1 code implementation • 2 Feb 2020 • Marian Kleineberg, Matthias Fey, Frank Weichert
This work presents a generative adversarial architecture for generating three-dimensional shapes based on signed distance representations.
2 code implementations • ICLR 2020 • Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.
Ranked #12 on Entity Alignment on DBP15k zh-en (using extra training data)
1 code implementation • 9 Apr 2019 • Matthias Fey
We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs.
Ranked #28 on Node Classification on Citeseer
4 code implementations • 6 Mar 2019 • Matthias Fey, Jan Eric Lenssen
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
Ranked #4 on Graph Classification on REDDIT-B
1 code implementation • 4 Oct 2018 • Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, Martin Grohe
We show that GNNs have the same expressiveness as the $1$-WL in terms of distinguishing non-isomorphic (sub-)graphs.
Ranked #4 on Graph Classification on NCI1
1 code implementation • NeurIPS 2018 • Jan Eric Lenssen, Matthias Fey, Pascal Libuschewski
We present group equivariant capsule networks, a framework to introduce guaranteed equivariance and invariance properties to the capsule network idea.
no code implementations • 16 Feb 2018 • Nils M. Kriege, Matthias Fey, Denis Fisseler, Petra Mutzel, Frank Weichert
To this end, the distance measure is used to implement a nearest neighbor classifier leading to a high computational cost for the prediction phase with increasing training set size.
5 code implementations • CVPR 2018 • Matthias Fey, Jan Eric Lenssen, Frank Weichert, Heinrich Müller
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e. g., graphs or meshes.
Ranked #3 on Node Classification on Cora