Graph neural networks ​

Introduction to Graph Machine Learning, Everything is Connected: Graph Neural Networks & Attending to Graph Transformers are great reads.

DIG library is neat.

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)
• Awesome Expressive GNN
• Pure Transformers are Powerful Graph Learners (2022) (Code)
• Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs (2021) (Code)
• Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs (2020) (Code)
• M3GNet - Materials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.
• Memory-Based Graph Networks (2020) (Code)
• Contrastive Multi-View Representation Learning on Graphs (2020) (Code)
• Tree Mover's Distance for Graphs: Bridging Graph Metrics and Stability of Graph Neural Networks (2022)
• SuperGlue: Learning Feature Matching with Graph Neural Networks (2020) (Code)
• Variational Graph Normalized Auto-Encoders (2021) (Code)
• GOOD: A Graph Out-of-Distribution Benchmark (2022)
• Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries (2022) (Code)
• Graph machine learning with missing node features (2022)
• PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images (2021) (Code)
• Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs (2022) (Code) (Code)
• Universal Graph Transformer Self-Attention Networks (2022)
• Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
• E3NN.jl - Julia implementation of Euclidean neural networks.
• GNN4Traffic - Collection of Graph Neural Network for Traffic Forecasting.
• Introduction to Graph Machine Learning (2023)
• Everything is Connected: Graph Neural Networks (2023)
• Graph Diffusion Convolution, as proposed in "Diffusion Improves Graph Learning" (2019)
• DiGress: Discrete Denoising diffusion models for graph generation (2023)
• Attending to Graph Transformers (2023) (Code)
• Awesome Graph Transformer
• Awesome Graph Neural Network Systems
• python_graphs - Static analysis library for computing graph representations of Python programs suitable for use with graph neural networks.
• Gated Graph Sequence Neural Networks (2015) (Code)
• Multi-State RNA Design with Geometric Multi-Graph Neural Networks (2023) (Code)
• Atomistic Line Graph Neural Network
• Hypergraph Survey - Curated list of Hypergraph Learning, Hypergraph Theory, Hypergraph Dataset and Hypergraph Tool.
• Vectorizing Graph Neural Networks (2020) (HN)
• Graphium - Scaling molecular GNNs to infinity.
• graphite - Implementing graph network models based on atomic structures.
• Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs (2023) (Code)
• Interpretable Graph Neural Networks for Tabular Data (2023) (HN)
• Graph Network Simulator (GNS) and MeshNet