Graph neural networks

Links#

• A practical introduction to GNNs (2021) ↗
• Spektral ↗ - Graph Neural Networks with Keras and Tensorflow 2. (Docs ↗)
• A Comprehensive Survey on Graph Neural Networks (2019) ↗
• Graph Neural Networks in TF2 ↗
• Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels (2019) ↗
• Strategies for Pre-training Graph Neural Networks ↗
• Transformers are Graph Neural Networks (2020) ↗ (HN ↗)
• Towards understanding glasses with graph neural networks (2020) ↗
• How Powerful are Graph Neural Networks? ↗
• Resources for learning Graph Neural Networks for beginners (2020) ↗
• Graph-based Deep Learning Literature ↗
• PyTorch Cluster ↗ - PyTorch Extension Library of Optimized Graph Cluster Algorithms.
• Graph Neural Network Model in TensorFlow ↗
• Traffic prediction with advanced Graph Neural Networks (2020) ↗ (HN ↗)
• Transformers Are Graph Neural Networks (2020) ↗ (HN ↗)
• Must-read papers on graph neural networks ↗
• Latest developments in Graph Neural Networks: A list of recent conference talks (2020) ↗
• DGL-LifeSci ↗ - Python package for graph neural networks in chemistry and biology.
• Introduction to Graph Neural Networks (2020) ↗
• PyDGN ↗ - Python library for Deep Graph Networks.
• A gentle introduction to deep learning for graphs (2020) ↗
• Graph Structure of Neural Networks ↗ - PyTorch implementation.
• GraphGym ↗ - Platform for designing and evaluating Graph Neural Networks.
• GraphRNN ↗ - Generating Realistic Graphs with Deep Auto-regressive Model.
• Position-aware Graph Neural Networks ↗
• SEAL - Learning from Subgraphs, Embeddings, and Attributes for Link prediction ↗
• Jraph ↗ - Lightweight library for working with graph neural networks in jax.
• Benchmarking Graph Neural Networks (2020) ↗ (Code ↗)
• Pro-GNN ↗ - PyTorch implementation of “Graph Structure Learning for Robust Graph Neural Networks”.
• Supervised Learning on Relational Databases with Graph Neural Networks ↗
• Why I’m lukewarm on graph neural networks (2020) ↗ (HN ↗)
• Simplicial Neural Networks ↗ - Generalization of graph neural networks to data that live on a class of topological spaces called [simplicial complexes].
• FLAG: Adversarial Data Augmentation for Graph Neural Networks ↗
• Distilling Knowledge From Graph Convolutional Networks (2020) ↗ (Code ↗)
• GN-Transformer AST ↗ - Code for “GN-Transformer: Fusing AST and Source Code information in Graph Networks” paper.
• Graph theory, graph convolutional networks, knowledge graphs (2021) ↗ (HN ↗)
• Theoretical Foundations of Graph Neural Networks (2021) ↗
• PyTorch GAT ↗ - PyTorch implementation of the original GAT paper.
• Graph Transformer Networks (2019) ↗ (Code ↗)
• DropEdge: Towards Deep Graph Convolutional Networks on Node Classification ↗
• DIG (Dive into Graphs) ↗ - Library for graph deep learning research.
• Understanding Graph Neural Networks from Graph Signal Denoising Perspectives (2020) ↗ (Code ↗)
• Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions ↗
• Graph Convolutional Networks in PyTorch ↗
• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges (2021) ↗
• E(n) Equivariant Graph Neural Networks (2021) ↗ (Code ↗)
• How Attentive are Graph Attention Networks? (2021) ↗ (Code ↗)
• Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification (2021) ↗ (Code ↗)
• Binary Graph Neural Networks (2021) ↗ (Code ↗)
• Scaling Graph Neural Networks with Approximate PageRank (2020) ↗ (Code ↗)
• CS224W: Machine Learning with Graphs (2021) ↗
• Graph Attention Networks (GAT) annotated implementation ↗
• Awesome Explainable Graph Reasoning ↗ - Collection of research papers and software related to explainability in graph machine learning.
• An Attempt at Demystifying Graph Deep Learning ↗
• Graph Random Neural Network for Semi-Supervised Learning on Graphs (2020) ↗ (Code ↗)
• CapsGNN: Capsule Graph Neural Networks in PyTorch ↗
• A Gentle Introduction to Graph Neural Networks (2021) ↗
• Understanding Convolutions on Graphs (2021) ↗
• GraphNeuralNetworks.jl ↗ - Graph Neural Networks in Julia.
• MilaGraph ↗ - Research group focusing on graph representation learning and graph neural networks.
• Modeling Relational Data with Graph Convolutional Networks (2017) ↗ (Code ↗)
• GNNLens2 ↗ - Visualization tool for Graph Neural Networks.
• Hierarchical Graph Representation Learning with Differentiable Pooling (2018) ↗ (Code ↗)
• VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization (2021) ↗
• Pitfalls of Graph Neural Network Evaluation (2019) ↗ (Code ↗)
• Understanding Pooling in Graph Neural Networks (2021) ↗ (Code ↗)
• Spectral Clustering with Graph Neural Networks for Graph Pooling (2020) ↗ (Code ↗)
• Graph Robustness Benchmark (GRB) ↗ - Scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph Machine Learning.
• TensorFlow GNN ↗ - Library to build Graph Neural Networks on the TensorFlow platform. (Article ↗)
• Graph Neural Networks through the lens of Differential Geometry and Algebraic Topology (2021) ↗ (Tweet ↗)
• DGN ↗ - Graph convolutional reinforcement learning, where the multi-agent environment is modeled as a graph, each agent is a node, and the encoding of local observation of agent is the feature of node.
• SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials (2021) ↗ (Tweet ↗)
• On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features (2021) ↗ (Tweet ↗)
• Graph Neural Networks as Neural Diffusion PDEs (2021) ↗
• PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT ↗
• Exact Combinatorial Optimization with Graph Convolutional Neural Networks (2021) ↗ (Code ↗)
• A Recipe for Training Neural Networks (2019) ↗
• GraphSAINT: Graph Sampling Based Inductive Learning Method (2020) ↗ (Code ↗)
• Decoupling the Depth and Scope of Graph Neural Networks (2021) ↗ (Code ↗)
• How to Scale Up GNNs with Mini-Batch Sampling (2021) ↗
• Papers about explainability of GNNs ↗
• GraphGallery ↗ - Gallery for benchmarking Graph Neural Networks (GNNs).
• From Canonical Correlation Analysis to Self-supervised Graph Neural Networks (2021) ↗ (Code ↗)
• Expressive Power of Invariant and Equivariant Graph Neural Networks (2021) ↗ (Code ↗)
• Simple implementation of Equivariant GNN in PyTorch ↗
• GemNet: Universal Directional Graph Neural Networks for Molecules (2021) ↗ (Code ↗)
• Graph4NLP ↗ - Easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language Processing.
• GNNs Recipe ↗ - Recipe to study Graph Neural Networks (GNNs).
• GraphiT: Encoding Graph Structure in Transformers (2021) ↗ (Code ↗)
• Graph Neural Networks with Learnable Structural and Positional Representations (2022) ↗ (Code ↗)
• Deep Learning on Graphs Book ↗
• Graph Neural Networks: Foundations, Frontiers, and Applications (2022) ↗
• Representing Long-Range Context for Graph Neural Networks with Global Attention ↗
• CW Networks ↗ - Message Passing Neural Networks for Simplicial and Cell Complexes.
• GMAN: A Graph Multi-Attention Network for Traffic Prediction ↗
• Awesome Efficient Graph Neural Networks ↗
• GraphSAGE ↗ - Inductive Representation Learning on Large Graphs. (PyTorch Code ↗)
• Topological Graph Neural Networks (2022) ↗
• Heterogeneous Graph Neural Network ↗
• Graph Condensation for Graph Neural Networks (2022) ↗ (Code ↗)
• Awesome Self Supervised GNN ↗ - Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).
• ptgnn ↗ - PyTorch Graph Neural Network Library.
• BrainGB ↗ - Unified, modular, scalable, and reproducible framework established for brain network analysis with GNNs. (Web ↗)
• Equilibrium Aggregation (2022) ↗
• Awesome resources on Graph Neural Networks ↗
• Graph Neural Networks with convolutional ARMA filters (2021) ↗ (Code ↗)
• Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation (2021) ↗ (Code ↗)
• Geometric and Physical Quantities Improve E(3) Equivariant Message Passing (2021) ↗ (Code ↗)
• Graph Attention Networks (2018) ↗ (Code ↗)
• Directed Acyclic Graph Neural Networks (2022) ↗ (Code ↗)
• Expressive GNNs and How To Tame Them (2022) ↗ (Tweet ↗)
• Automated Self-Supervised Learning for Graphs (2022) ↗ (Code ↗)
• Graph Neural PDEs ↗
• Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs (2022) ↗ (Review ↗)
• Sampling for Heterogeneous GNNs ↗
• TensorFlow implementations of Graph Neural Networks ↗
• gtrick ↗ - Bag of Tricks for Graph Neural Networks.
• How Airbnb is using Graph Convolutional Networks in production (2022) ↗
• Foundations of Graph Neural Networks online course ↗
• Basics of Graph Neural Networks ↗
• Local Augmentation for Graph Neural Networks (2021) ↗ (Code ↗)