There are 2 repositories under graph-learning topic.
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)
[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
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."
An SDK for multi-agent collaborative perception.
A Large-Scale Company Relation Graph for Investment Industry
Paper List for Fair Graph Learning (FairGL).
GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding
Topological Graph Neural Networks (ICLR 2022)
[ICLR 2022] Data-Efficient Graph Grammar Learning for Molecular Generation
Multi-modal Graph learning for Disease Prediction (IEEE Trans. on Medical imaging, TMI2022)
A real-world dataset for EV-related research, e.g., spatiotemporal prediction and urban energy management.
Edge-Augmented Graph Transformer
[ICLR 2022] Graph-Relational Domain Adaptation
Multi-class Classification with fine-tuned BERT & GNN
The official repository of "GraphSPD: Graph-Based Security Patch Detection with Enriched Code Semantics". The paper will appear in the IEEE Symposium on Security and Privacy (S&P), San Francisco, CA, May 22-26, 2023.
Official implementation for paper "Can Graph Learning Improve Task Planning?" https://arxiv.org/abs/2405.19119