1.3k citations · 2.2k across the 14 of their papers we have counts for
16 papers
Relational Deep Learning: Graph Representation Learning on Relational Databases
Matthias Fey, Weihua Hu, Kexin Huang +6
Much of the world's most valued data is stored in relational databases and data warehouses, where the data is organized into many tables connected by primary-foreign key relations.…
Temporal Graph Benchmark for Machine Learning on Temporal Graphs
Shenyang Huang, Farimah Poursafaei, Jacob Danovitch +7
We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning mod…
Weisfeiler and Leman go Machine Learning: The Story so far
Christopher Morris, Yaron Lipman, Haggai Maron +5
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…
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings
Matthias Fey, Jan E. Lenssen, Frank Weichert +1
We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs to large graphs. GAS prunes entire sub-trees of the computation graph by utilizing historical…
The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs
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 f…
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Hongyu Ren +3
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 a…
Hierarchical Inter-Message Passing for Learning on Molecular Graphs
Matthias Fey, Jan-Gin Yuen, Frank Weichert
We present a hierarchical neural message passing architecture for learning on molecular graphs. Our model takes in two complementary graph representations: the raw molecular graph…
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Weihua Hu, Matthias Fey, Marinka Zitnik +5
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…
Adversarial Generation of Continuous Implicit Shape Representations
Marian Kleineberg, Matthias Fey, Frank Weichert
This work presents a generative adversarial architecture for generating three-dimensional shapes based on signed distance representations. While the deep generation of shapes has b…
Deep Graph Matching Consensus
Matthias Fey, Jan E. Lenssen, Christopher Morris +2
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs. First, we use localized node embeddings computed by a graph…
Fast Graph Representation Learning with PyTorch Geometric
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. In addition to gene…
Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks
Matthias Fey
We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs. In contrast to current graph neural networks w…
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
Christopher Morris, Martin Ritzert, Matthias Fey +4
In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a supervised, end-to-end fashion…
Group Equivariant Capsule Networks
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. Our work can be divided into…
Recognizing Cuneiform Signs Using Graph Based Methods
Nils M. Kriege, Matthias Fey, Denis Fisseler +2
The cuneiform script constitutes one of the earliest systems of writing and is realized by wedge-shaped marks on clay tablets. A tremendous number of cuneiform tablets have already…
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
Matthias Fey, Jan Eric Lenssen, Frank Weichert +1
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. Our main…