#计算机科学#A distributed graph deep learning framework.
Paddle Graph Learning (PGL) is an efficient and flexible graph learning framework based on PaddlePaddle
#计算机科学#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.
Extensible Surrogate Potential of Ab initio Learned and Optimized by Message-passing Algorithm 🍹https://arxiv.org/abs/2010.01196
#计算机科学#Code & data accompanying the NeurIPS 2020 paper "Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings".
#计算机科学#Advances on machine learning of graphs, covering the reading list of recent top academic conferences.
A curated list of resources on cold-start recommendations.
A real-world dataset for EV-related research, e.g., spatiotemporal prediction and urban energy management.
A Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).
Neuro-symbolic interpretation learning (mostly just language-learning, for now)
#计算机科学#An SDK for multi-agent collaborative perception.
#计算机科学#[NeurIPS2021] Learning Distilled Collaboration Graph for Multi-Agent Perception
Code for the SIGGRAPH 2022 paper "DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds."