#Graph-neural-network
Showing 25 of 25 repositories tagged #graph-neural-network, ranked by stars
A library for graph deep learning research
A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)
Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).
A repository of pretty cool datasets that I collected for network science and machine learning research.
Deep and conventional community detection related papers, implementations, datasets, and tools.
CRSLab is an open-source toolkit for building Conversational Recommender System (CRS).
Implementation of E(n)-Equivariant Graph Neural Networks, in Pytorch
Awesome Resources for Advanced Computer Vision Topics
Graph Neural Network based Social Recommendation Model. SIGIR2019.
Graph Neural Network application in predicting AC Power Flow calculation. Developed with Pytorch Geometric framework. My Master Thesis at Eindhoven University of Technology
A Deep learning library for neutrino telescopes
Deep Vessel Segmentation by Learning Graphical Connectivity
MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems, KDD2021
Readings for "A Unified View of Relational Deep Learning for Drug Pair Scoring." (IJCAI 2022)
Collection of resources about partial differential equations, graph neural networks, deep learning and dynamical system simulation
Bipartite-network link prediction in Python
Tutorials about AutoML
Learning to Drop: Robust Graph Neural Network via Topological Denoising & Robust Graph Representation Learning via Neural Sparsification
Gigantic Graph Learning (GiGL) Framework: Large-scale training and inference for Graph Neural Networks
The official PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf '21)
[NeurIPS 2024 🔥] TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs
Resource for "A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective"
A Fortran-based neural network library for physics-based applications. Alongside standard neural network layer types, it also supports graph-based layers and physics informed neural networks.
Repository for advanced traffic forecasting models integrating GCN, LSTM/Bi-LSTM, and attention mechanisms for improved accuracy, including weather data processing.
GNN4ID: A Toolset for Crafting Graph Neural Network-Based NIDS Datasets