Search Results for author: Matthias Fey

Found 16 papers, 12 papers with code

Relational Deep Learning: Graph Representation Learning on Relational Databases

no code implementations7 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.

Feature Engineering Graph Representation Learning

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

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.

Node Property Prediction Property Prediction

Weisfeiler and Leman go Machine Learning: The Story so far

no code implementations18 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.

BIG-bench Machine Learning Representation Learning

GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings

1 code implementation10 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.

The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs

no code implementations12 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.

BIG-bench Machine Learning Node Classification

OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

6 code implementations17 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.

BIG-bench Machine Learning Graph Learning +4

Hierarchical Inter-Message Passing for Learning on Molecular Graphs

1 code implementation22 Jun 2020 Matthias Fey, Jan-Gin Yuen, Frank Weichert

We present a hierarchical neural message passing architecture for learning on molecular graphs.

Open Graph Benchmark: Datasets for Machine Learning on Graphs

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.

Knowledge Graphs Node Property Prediction

Adversarial Generation of Continuous Implicit Shape Representations

1 code implementation2 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.

Deep Graph Matching Consensus

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)

Entity Alignment Graph Matching +2

Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks

1 code implementation9 Apr 2019 Matthias Fey

We propose a dynamic neighborhood aggregation (DNA) procedure guided by (multi-head) attention for representation learning on graphs.

General Classification Node Classification +1

Fast Graph Representation Learning with PyTorch Geometric

4 code implementations6 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.

Graph Classification Graph Representation Learning +2

Group Equivariant Capsule Networks

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.

Recognizing Cuneiform Signs Using Graph Based Methods

no code implementations16 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.

SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

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.

General Classification Graph Classification +2

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