Awesome-Cold-Start-Recommendation

A distributed graph deep learning framework.

High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.

Training neural models with structured signals.

[SIGIR'2024] "GraphGPT: Graph Instruction Tuning for Large Language Models"

Code for the paper "PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks" (ICPR 2020)

[WSDM'2024 Oral] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"

[EMNLP'2024] "OpenGraph: Towards Open Graph Foundation Models"

PyTorch Library for Low-Latency, High-Throughput Graph Learning on GPUs.

Code & data accompanying the NeurIPS 2020 paper "Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings".

"AnyGraph: Graph Foundation Model in the Wild"