graph-machine-learning
There are 67 repositories under graph-machine-learning topic.
stellargraph/stellargraph
StellarGraph - Machine Learning on Graphs
snap-stanford/ogb
Benchmark datasets, data loaders, and evaluators for graph machine learning
daiquocnguyen/Graph-Transformer
Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)
mims-harvard/PrimeKG
Precision Medicine Knowledge Graph (PrimeKG)
michiyasunaga/LinkBERT
[ACL 2022] LinkBERT: A Knowledgeable Language Model 😎 Pretrained with Document Links
lukecavabarrett/pna
Implementation of Principal Neighbourhood Aggregation for Graph Neural Networks in PyTorch, DGL and PyTorch Geometric
zhao-tong/graph-data-augmentation-papers
A curated list of graph data augmentation papers.
neo4j/graph-data-science-client
A Python client for the Neo4j Graph Data Science (GDS) library
mims-harvard/GraphXAI
GraphXAI: Resource to support the development and evaluation of GNN explainers
Graph-Machine-Learning-Group/grin
Official repository for the paper "Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks" (ICLR 2022)
chenxuhao/ReadingList
Papers on Graph Analytics, Mining, and Learning
NYU-MLDA/OpenABC
OpenABC-D is a large-scale labeled dataset generated by synthesizing open source hardware IPs. This dataset can be used for various graph level prediction problems in chip design.
Saro00/DGN
Implementation of Directional Graph Networks in PyTorch and DGL
benedekrozemberczki/tigerlily
TigerLily: Finding drug interactions in silico with the Graph.
cptq/SignNet-BasisNet
SignNet and BasisNet
arangoml/fastgraphml
Given an input graph (ArangoDB or PyG) it generates graph embeddings using Low-Code framework built on top of PyG.
DM2-ND/CFLP
Author: Tong Zhao (tzhao2@nd.edu). ICML 2022. Learning from Counterfactual Links for Link Prediction
apache/incubator-hugegraph-ai
The integration of HugeGraph with AI/LLM & GraphRAG
oracle-samples/pgx-samples
Applications using Parallel Graph AnalytiX (PGX) from Oracle Labs
sunfanyunn/vGraph
Official code for "vGraph: A Generative Model for Joint CommunityDetection and Node Representation Learning" (NeurIPS 2019)
sunfanyunn/graph-classification
A collection of graph classification methods
sachinsharma9780/Build-ML-pipelines-for-Computer-Vision-NLP-and-Graph-Neural-Networks-using-Nvidia-Triton-Server
Build ML pipelines for Computer Vision, NLP and Graph Neural Networks using Triton Server.
AndrewSpano/Stanford-CS224W-ML-with-Graphs
Solutions to assignments of the CS224W Machine Learning with Graphs course from Stanford University.
caspervanengelenburg/msd
Code repository for the ECCV paper "MSD: A Benchmark Dataset for Floor Plan of Building Complexes".
HipGraph/FusedMM
Implementation of FusedMM method for IPDPS 2021 paper titled "FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks"
Orbifold/pyg-link-prediction
Pytorch Geometric link prediction of a homogeneous social graph.
Zhuofeng-Li/TEG-Benchmark
[NeurIPS 2024 🔥] TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs
ZhiningLiu1998/BAT
[ICML'24] BAT: 🚀 Boost Class-imbalanced Node Classification with <10 lines of Code | 从拓扑视角出发10行代码改善类别不平衡节点分类
isadrtdinov/bootcamp-idao-2022
IDAO 2022: Machine Learning Bootcamp
karannb/cs224w
Solutions to homework problems and programming assignments for Stanford's cs224w Machine Learning with Graphs (2021) course.
chenxuhao/GraphAIBench
A benchmark suite for Graph Machine Learning
zahta/graph_ml
Course: Graph Machine Learning focuses on the application of machine learning algorithms on graph-structured data. Some of the key topics that are covered in the course include graph representation learning and graph neural networks, algorithms for the world wide web, reasoning over knowledge graphs, and social network analysis.
RomanoLab/comptox_ai
ComptoxAI - An artificial Intelligence toolkit for computational toxicology
tong-wu-umn/ME2Vec
Source code of ME2Vec.
LamineTourelab/Tutorial
Tutorials on machine learning, artificial intelligence in general and in biomedical research.
yandex-research/structural-graph-shifts
New structural distributional shifts for evaluating graph models