A distributed graph deep learning framework.
-
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.
Code for the paper "PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks" (ICPR 2020)
Training neural models with structured signals.
A Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).
PyTorch Library for Low-Latency, High-Throughput Graph Learning on GPUs.
[SIGIR'2024] "GraphGPT: Graph Instruction Tuning for Large Language Models"
[WSDM'2024 Oral] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"
Neuro-symbolic interpretation learning (mostly just language-learning, for now)
Code & data accompanying the NeurIPS 2020 paper "Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings".
[ICLR 2022] Data-Efficient Graph Grammar Learning for Molecular Generation
Extensible Surrogate Potential of Ab initio Learned and Optimized by Message-passing Algorithm 🍹https://arxiv.org/abs/2010.01196
Topological Graph Neural Networks (ICLR 2022)
MANDO is a new heterogeneous graph representation to learn the heterogeneous contract graphs' structures to accurately detect vulnerabilities in smart contract source code at both coarse-grained contract-level and fine-grained line-level.
An SDK for multi-agent collaborative perception.
[NeurIPS2021] Learning Distilled Collaboration Graph for Multi-Agent Perception
Advances on machine learning of graphs, covering the reading list of recent top academic conferences.
GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding
"OpenGraph: Towards Open Graph Foundation Models"
Add a description, image, and links to the graph-learning topic page so that developers can more easily learn about it.
To associate your repository with the graph-learning topic, visit your repo's landing page and select "manage topics."