Trending repositories for topic graph-neural-networks
Anomaly detection related books, papers, videos, and toolboxes
Python package built to ease deep learning on graph, on top of existing DL frameworks.
A curated list of graph-based fraud, anomaly, and outlier detection papers & resources
A Python Library for Graph Outlier Detection (Anomaly Detection)
[NeurIPS 2023] DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
Paper list about hyperbolic embedding, hyperbolic models,hyperbolic applications
Free hands-on course about Graph Neural Networks using PyTorch Geometric.
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
links to conference publications in graph-based deep learning
[KDD'2024 Survey+Tutorial] "LLM4Graph: A Survey of Large Language Models for Graphs"
[NeurIPS 2022] DRAGON 🐲: Deep Bidirectional Language-Knowledge Graph Pretraining
Geometric GNN Dojo provides unified implementations and experiments to explore the design space of Geometric Graph Neural Networks.
[SIGIR'2024] "GraphGPT: Graph Instruction Tuning for Large Language Models"
Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).
[KDD'2024 Survey+Tutorial] "LLM4Graph: A Survey of Large Language Models for Graphs"
[NeurIPS 2023] DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
A Comprehensive Survey of Self-Supervised Learning for Recommendation
Paper list about hyperbolic embedding, hyperbolic models,hyperbolic applications
Segment Anything Model for large-scale, vectorized road network extraction from aerial imagery. CVPRW 2024
Free hands-on course about Graph Neural Networks using PyTorch Geometric.
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
A scalable graph learning toolkit for extremely large graph datasets. (WWW'22, 🏆 Best Student Paper Award)
[NeurIPS 2022] DRAGON 🐲: Deep Bidirectional Language-Knowledge Graph Pretraining
图深度学习（葡萄书），在线阅读地址： https://datawhalechina.github.io/grape-book
A deep reinforcement learning (DRL) based approach for spatial layout of land use and roads in urban communities. (Nature Computational Science)
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Pretrained universal neural network potential for charge-informed atomistic modeling https://chgnet.lbl.gov
[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Anomaly detection related books, papers, videos, and toolboxes
[KDD'2024 Survey+Tutorial] "LLM4Graph: A Survey of Large Language Models for Graphs"
A curated list of graph-based fraud, anomaly, and outlier detection papers & resources
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
🔨 🍇 💻 🚀 GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba | 一站式图计算系统
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
Paper list about hyperbolic embedding, hyperbolic models,hyperbolic applications
Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).
Free hands-on course about Graph Neural Networks using PyTorch Geometric.
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Segment Anything Model for large-scale, vectorized road network extraction from aerial imagery. CVPRW 2024
A Python Library for Graph Outlier Detection (Anomaly Detection)
Code for TKDE paper "Self-supervised learning on graphs: Contrastive, generative, or predictive"
[KDD'2024 Survey+Tutorial] "LLM4Graph: A Survey of Large Language Models for Graphs"
Contextualizing protein representations using deep learning on protein networks and single-cell data
Segment Anything Model for large-scale, vectorized road network extraction from aerial imagery. CVPRW 2024
A Comprehensive Survey of Self-Supervised Learning for Recommendation
[NeurIPS 2023] DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
Paper list about hyperbolic embedding, hyperbolic models,hyperbolic applications
[WWW'2024] "GraphPro: Graph Pre-training and Prompt Learning for Recommendation"
Free hands-on course about Graph Neural Networks using PyTorch Geometric.
Dir-GNN is a machine learning model that enables learning on directed graphs.
Advances on machine learning of graphs, covering the reading list of recent top academic conferences.
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
A repository contains a collection of resources and papers on Imbalance Learning On Graphs
Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).
[AAAI23] This it the official github for AAAI23 paper "Spatio-Temporal Meta-Graph Learning for Traffic Forecasting"
[KDD'2024 Survey+Tutorial] "LLM4Graph: A Survey of Large Language Models for Graphs"
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Anomaly detection related books, papers, videos, and toolboxes
[KDD'2024 Survey+Tutorial] "LLM4Graph: A Survey of Large Language Models for Graphs"
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
links to conference publications in graph-based deep learning
A curated list of graph-based fraud, anomaly, and outlier detection papers & resources
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
🔨 🍇 💻 🚀 GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba | 一站式图计算系统
Segment Anything Model for large-scale, vectorized road network extraction from aerial imagery. CVPRW 2024
📈 Awesome resources related to GNNs for Time Series Analysis (GNN4TS) 🔥 https://arxiv.org/abs/2307.03759
A Comprehensive Survey of Self-Supervised Learning for Recommendation
Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).
[KDD'2024 Survey+Tutorial] "LLM4Graph: A Survey of Large Language Models for Graphs"
Mixed continous/categorical flow-matching model for de novo molecule generation.
Segment Anything Model for large-scale, vectorized road network extraction from aerial imagery. CVPRW 2024
A Comprehensive Survey of Self-Supervised Learning for Recommendation
DrivAerNet: A Parametric Car Dataset for Data-driven Aerodynamic Design and Graph-Based Drag Prediction
Next-generation scheduling problem solver based on GNNs and Reinforcement Learning
[NeurIPS'2023] "GPT-ST: Generative Pre-Training of Spatio-Temporal Graph Neural Networks"
GraphTranslator:Aligning Graph Model to Large Language Model for Open-ended Tasks
[WSDM'2024 Oral] "DiffKG: Knowledge Graph Diffusion Model for Recommendation"
Advances on machine learning of graphs, covering the reading list of recent top academic conferences.
[IJCAI 2024] Papers about graph reduction including graph coarsening, graph condensation, graph sparsification, graph summarization, etc.
Code for our paper: Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
[NeurIPS 2023] DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
[KDD'2023] "KGRec: Knowledge Graph Self-Supervised Rationalization for Recommendation"
A repository contains a collection of resources and papers on Imbalance Learning On Graphs
📈 Awesome resources related to GNNs for Time Series Analysis (GNN4TS) 🔥 https://arxiv.org/abs/2307.03759
[SIGIR'2024] "GraphGPT: Graph Instruction Tuning for Large Language Models"
[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
🔥 CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantification
[IJCAI 2024] Papers about graph reduction including graph coarsening, graph condensation, graph sparsification, graph summarization, etc.
Segment Anything Model for large-scale, vectorized road network extraction from aerial imagery. CVPRW 2024
A Comprehensive Survey of Self-Supervised Learning for Recommendation
Automated Parallelization System and Infrastructure for Multiple Ecosystems
Uncertainty Quantification over Graph with Conformalized Graph Neural Networks (NeurIPS 2023)
[KDD'2024 Survey+Tutorial] "LLM4Graph: A Survey of Large Language Models for Graphs"
Official source code for "Graph Neural Networks for Learning Equivariant Representations of Neural Networks". In ICLR 2024 (oral).
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Anomaly detection related books, papers, videos, and toolboxes
🔨 🍇 💻 🚀 GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba | 一站式图计算系统
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
links to conference publications in graph-based deep learning
📈 Awesome resources related to GNNs for Time Series Analysis (GNN4TS) 🔥 https://arxiv.org/abs/2307.03759
[SIGIR'2024] "GraphGPT: Graph Instruction Tuning for Large Language Models"
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).
A curated list of graph-based fraud, anomaly, and outlier detection papers & resources
A Python Library for Graph Outlier Detection (Anomaly Detection)
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).
All in One: Multi-task Prompting for Graph Neural Networks, KDD 2023.
[ICLR'24] Enhancing Healthcare Predictions with Personalized Knowledge Graphs
[ICLR'24] EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
A deep reinforcement learning (DRL) based approach for spatial layout of land use and roads in urban communities. (Nature Computational Science)
A curated list of papers on pre-training for graph neural networks (Pre-train4GNN).
Advances on machine learning of graphs, covering the reading list of recent top academic conferences.
[NeurIPS 2023] DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
图深度学习（葡萄书），在线阅读地址： https://datawhalechina.github.io/grape-book
A Comprehensive Survey of Self-Supervised Learning for Recommendation
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
Dir-GNN is a machine learning model that enables learning on directed graphs.
Awesome Temporal Graph Learning is a collection of SOTA, novel temporal graph learning methods (papers, codes, and datasets).
Automated Parallelization System and Infrastructure for Multiple Ecosystems
Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation
Advances on machine learning of dynamic (temporal) graphs, covering the reading list of recent top academic conferences.
Uncertainty Quantification over Graph with Conformalized Graph Neural Networks (NeurIPS 2023)
[KDD'2024 Survey+Tutorial] "LLM4Graph: A Survey of Large Language Models for Graphs"
[ICDE'2023] When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks