Stars
KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019
An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)
A deep matching model library for recommendations & advertising. It's easy to train models and to export representation vectors which can be used for ANN search.
Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG. Easy integration in existing products with customisation! Any LLM: GPT4, Groq, Llama. Any Vectorstore: …
Pytorch implementation of paper 'GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks' to appear on WSDM2021
Implementation for Simple Spectral Graph Convolution in ICLR 2021
Framework for evaluating Graph Neural Network models on semi-supervised node classification task
official implementation for the paper "Simplifying Graph Convolutional Networks"
Graph Convolutional Neural Networks with Complex Rational Spectral Filters
PyTorch Implement for Path Attention Graph Network
PPNP & APPNP models from "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019)
A PyTorch implementation of "Predict then Propagate: Graph Neural Networks meet Personalized PageRank" (ICLR 2019).
Implementation of Graph Convolutional Networks in TensorFlow
An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).
[NeurIPS 2021]: Improve the GNN expressivity and scalability by decoupling the depth and receptive field of state-of-the-art GNN architectures
links to conference publications in graph-based deep learning
A set of jupyter notebooks for the practice of TDA with the python Gudhi library together with popular machine learning and data sciences libraries.
Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"
[IJCNN 2021] Node Embedding using Mutual Information and Self-Supervision based Bi-level Aggregation
Code for NCAA paper "Multi-level Disentanglement Graph Neural Network"
Code & data accompanying the NeurIPS 2020 paper "Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings".
Topological Graph Neural Networks (ICLR 2022)
reproduce ICLR2021 paper "AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models"
Repository for benchmarking graph neural networks (JMLR 2023)
The sample codes for our ICLR18 paper "FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling""