Stars
GRID: Generative Recommendation with Semantic IDs
Gigantic Graph Learning (GiGL) Framework: Large-scale training and inference for Graph Neural Networks
[WWW'25] GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems
Must-read papers on deep learning to hash (DeepHash)
GiGL is an open-source library for training and inference of Graph Neural Networks at very large (billion) scale.
Additional utils and helpers to extend TensorFlow when build recommendation systems, contributed and maintained by SIG Recommenders.
Open-source Windows and Office activator featuring HWID, Ohook, TSforge, KMS38, and Online KMS activation methods, along with advanced troubleshooting.
Open-source Library PyGDebias: Graph Datasets and Fairness-Aware Graph Mining Algorithms
The code for our KDD'23 paper: "CARL-G: Clustering-Accelerated Representation Learning on Graphs"
Official repository for NeurIPS'23 paper: GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation
[ICML 2023] Linkless Link Prediction via Relational Distillation
A playbook for systematically maximizing the performance of deep learning models.
[KDD'23] Source codes of "Semi-Supervised Graph Imbalanced Regression"
Enterprise graph machine learning framework for billion-scale graphs for ML scientists and data scientists.
[ICLR 2023] MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization
[ICLR'23] Implementation of "Empowering Graph Representation Learning with Test-Time Graph Transformation"
Official repository for ICLR'23 paper: Multi-task Self-supervised Graph Neural Network Enable Stronger Task Generalization
[ICLR 2023] Link Prediction with Non-Contrastive Learning
Official repository for EMNLP'22 paper: Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering
MLNLP: Paper Picture Writing Code
Iconic font aggregator, collection, & patcher. 3,600+ icons, 50+ patched fonts: Hack, Source Code Pro, more. Glyph collections: Font Awesome, Material Design Icons, Octicons, & more
Notebooks using the Hugging Face libraries 🤗
A curated list of graph data augmentation papers.
Official implementation of Neural Bellman-Ford Networks (NeurIPS 2021)
Adversarial attacks and defenses on Graph Neural Networks.
TNNLS: A Synergistic Approach for Graph Anomaly Detection with Pattern Mining and Feature Learning; CIKM'20: Error-bounded Graph Anomaly Loss for GNNs.