A distributed graph deep learning framework.
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Updated
Aug 19, 2023 - C++
A distributed graph deep learning framework.
Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle
High performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings.
Training neural models with structured signals.
Code for the paper "PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks" (ICPR 2020)
[SIGIR'2024] "GraphGPT: Graph Instruction Tuning for Large Language Models"
[WSDM'2024 Oral] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"
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".
Extensible Surrogate Potential of Ab initio Learned and Optimized by Message-passing Algorithm 🍹https://arxiv.org/abs/2010.01196
"OpenGraph: Towards Open Graph Foundation Models"
A Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).
Advances on machine learning of graphs, covering the reading list of recent top academic conferences.
Code for the SIGGRAPH 2022 paper "DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds."
[NeurIPS2021] Learning Distilled Collaboration Graph for Multi-Agent Perception
An SDK for multi-agent collaborative perception.
A Large-Scale Company Relation Graph for Investment Industry
Neuro-symbolic interpretation learning (mostly just language-learning, for now)
Paper List for Fair Graph Learning (FairGL).
"GraphEdit: Large Language Models for Graph Structure Learning"
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