Trending repositories for topic graph-neural-networks
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
Anomaly detection related books, papers, videos, and toolboxes
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"
Paper list about hyperbolic embedding, hyperbolic models,hyperbolic applications
A powerful and flexible machine learning platform for drug discovery
TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
Code & data accompanying the NeurIPS 2020 paper "Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings".
[KDD'2024] "LLM4Graph: A Survey of Large Language Models for Graphs"
[NAACL 2021] QAGNN: Question Answering using Language Models and Knowledge Graphs 🤖
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"
Paper list about hyperbolic embedding, hyperbolic models,hyperbolic applications
TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
Code & data accompanying the NeurIPS 2020 paper "Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings".
[KDD'2024] "LLM4Graph: A Survey of Large Language Models for Graphs"
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
[NAACL 2021] QAGNN: Question Answering using Language Models and Knowledge Graphs 🤖
A powerful and flexible machine learning platform for drug discovery
Anomaly detection related books, papers, videos, and toolboxes
SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
Graph Transformer Architecture. Source code for "A Generalization of Transformer Networks to Graphs", DLG-AAAI'21.
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
Anomaly detection related books, papers, videos, and toolboxes
SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
Python package built to ease deep learning on graph, on top of existing DL frameworks.
[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"
Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).
Paper list about hyperbolic embedding, hyperbolic models,hyperbolic applications
Papers about pretraining and self-supervised learning on Graph Neural Networks (GNN).
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
Mixed continous/categorical flow-matching model for de novo molecule generation.
TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
[KDD'2024] "LLM4Graph: A Survey of Large Language Models for Graphs"
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
Mixed continous/categorical flow-matching model for de novo molecule generation.
The unravelsports package aims to aid researchers, analysts and enthusiasts by providing intermediary steps in the complex process of turning raw sports data into meaningful information and actionable...
[NeurIPS 2024] Official implementation for paper "Can Graph Learning Improve Planning in LLM-based Agents?"
Official implementation for NeurIPS'24 paper "Generative Semi-supervised Graph Anomaly Detection"
TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
A curated list of papers on graph structure learning (GSL).
[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"
[NeurIPS 2023] DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization
Paper list about hyperbolic embedding, hyperbolic models,hyperbolic applications
[EMNLP2023] Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction
[ICLR 2023 Spotlight] Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
[WWW'2023] "DCRec: Debiased Contrastive Learning for Sequential Recommendation"
[KDD'2024] "LLM4Graph: A Survey of Large Language Models for Graphs"
A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
ST-SSL (STSSL): Spatio-Temporal Self-Supervised Learning for Traffic Flow Forecasting/Prediction
This is the official implementation of our mesh-based neural network (MESH2IR) to generate acoustic impulse responses (IRs) for indoor 3D scenes represented using a mesh.
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
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
SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
[TPAMI 2024] Awesome Resources of GNNs for Time Series Analysis (GNN4TS)
links to conference publications in graph-based deep learning
🔨 🍇 💻 🚀 GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba | 一站式图计算系统
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
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 Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
Mixed continous/categorical flow-matching model for de novo molecule generation.
The official implementation of DiffAbXL benchmarked in the paper "Exploring Log-Likelihood Scores for Ranking Antibody Sequence Designs", formerly titled "Benchmarking Generative Models for Antibody D...
Official implementation for NeurIPS'24 paper "Generative Semi-supervised Graph Anomaly Detection"
[NeurIPS 2024] Official implementation for paper "Can Graph Learning Improve Planning in LLM-based Agents?"
The unravelsports package aims to aid researchers, analysts and enthusiasts by providing intermediary steps in the complex process of turning raw sports data into meaningful information and actionable...
[TMLR 2024] Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields
Awesome graph neural networks for brain network learning. Collections of related research papers with implementations, commonly used datasets and tools. We also invite researchers interested in brain...
[EMNLP2023] Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction
TxGNN: Zero-shot prediction of therapeutic use with geometric deep learning and clinician centered design
Colab implementation for Fraud Detection in Graph Neural Networks, based on Deep Graph Library (DGL) and PyTorch backend.
Awesome Temporal Graph Learning is a collection of SOTA, novel temporal graph learning methods (papers, codes, and datasets).
[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"
[KDD'2023] "KGRec: Knowledge Graph Self-Supervised Rationalization for Recommendation"
This is the official repository for our paper KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning.
Code for the AAAI 2022 publication "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"
A modular Python library for creating, solving, and visualizing Job Shop Scheduling Problems.
A command line tool for extracting machine learning ready data from software binaries powered by Radare2
[KDD'2024] "LLM4Graph: A Survey of Large Language Models for Graphs"
A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
Colab Notebooks covering deep learning tools for biomolecular structure prediction and design
All graph/GNN papers accepted at the International Conference on Machine Learning (ICML) 2024.
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 2024] Official implementation for paper "Can Graph Learning Improve Planning in LLM-based Agents?"
GraphTranslator:Aligning Graph Model to Large Language Model for Open-ended Tasks
It is a comprehensive resource hub compiling all graph papers accepted at the International Conference on Learning Representations (ICLR) in 2024.
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.
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
Anomaly detection related books, papers, videos, and toolboxes
SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
Comprehensive and timely academic information on federated learning (papers, frameworks, datasets, tutorials, workshops)
[TPAMI 2024] Awesome Resources of GNNs for Time Series Analysis (GNN4TS)
links to conference publications in graph-based deep learning
[SIGIR'2024] "GraphGPT: Graph Instruction Tuning for Large Language Models"
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
A curated list of graph-based fraud, anomaly, and outlier detection papers & resources
[EMNLP'2024] "OpenGraph: Towards Open Graph Foundation Models"
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).
[KDD'2024] "LLM4Graph: A Survey of Large Language Models for Graphs"
"GraphEdit: Large Language Models for Graph Structure Learning"
A Comprehensive Survey of Self-Supervised Learning for Recommendation
All graph/GNN papers accepted at the International Conference on Machine Learning (ICML) 2024.
Mixed continous/categorical flow-matching model for de novo molecule generation.
Fusion of protein sequence and structural information, using denoising pre-training network for zero-shot protein engineering (eLife 2024).
[WWW'2024] "GraphPro: Graph Pre-training and Prompt Learning for Recommendation"
[IJCAI 2024] Papers about graph reduction including graph coarsening, graph condensation, graph sparsification, graph summarization, etc.
Anti Money Laundering Detection using Graph Attention Network
It is a comprehensive resource hub compiling all graph papers accepted at the International Conference on Learning Representations (ICLR) in 2024.
[CIKM'2024] "RecDiff: Diffusion Model for Social Recommendation"
[WSDM'2024 Oral] "DiffKG: Knowledge Graph Diffusion Model for Recommendation"
PoweFlowNet: Leveraging Message Passing GNNs for Improved Power Flow Approximation
A curated list of papers on graph structure learning (GSL).
[ICLR 2024] Official implementation of Spiking Graph Contrastive Learning (0️⃣1️⃣ SpikeGCL)
Code for our paper: Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
A command line tool for extracting machine learning ready data from software binaries powered by Radare2