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"AnyGraph: Graph Foundation Model in the Wild"
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"
[KDD'2024] "LLM4Graph: A Survey of Large Language Models for Graphs"
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".