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Force-directed graph rendered on HTML5 canvas
Graph Data Science: an abstraction layer in Python for building knowledge graphs, integrated with popular graph libraries – atop Pandas, NetworkX, RAPIDS, RDFlib, pySHACL, PyVis, morph-kgc, pslpyth…
A high performance Python graph library implemented in Rust.
Gel supercharges Postgres with a modern data model, graph queries, Auth & AI solutions, and much more.
Graph4nlp is the library for the easy use of Graph Neural Networks for NLP. Welcome to visit our DLG4NLP website (https://dlg4nlp.github.io/index.html) for various learning resources!
A tool to migrate an existing database to a graph database
The fastest pure-Python PEG parser I can muster
A GraphQL to SQL query execution layer for query planning and batch data fetching.
A pytorch adversarial library for attack and defense methods on images and graphs
Type safe TypeScript client for any GraphQL API
ONgDB is an independent fork of Neo4j® Enterprise Edition version 3.4.0.rc02 licensed under AGPLv3 and/or Community Edition licensed under GPLv3
Layout is a rust library and a tool that renders Graphviz dot files.
🐳 dockerdot shows dockerfile dependenciy graph. This is useful to understand how build dockerfile. This uses Go WebAssembly + BuildKit package.
Library extending Jupyter notebooks to integrate with Apache TinkerPop, openCypher, and RDF SPARQL.
Python library for implementing GraphQL servers using schema-first approach.
An incremental parsing system for programming tools
The flexible backend for all your projects 🐰 Turn your DB into a headless CMS, admin panels, or apps with a custom UI, instant APIs, auth & more.
PostgreSQL Languages AST and statements prettifier: master branch covers PG10, v2 branch covers PG12, v3 covers PG13, v4 covers PG14, v5 covers PG15, v6 covers PG16, v7 covers PG17
A curated list of Knowledge Graph related learning materials, databases, tools and other resources
Graph Attention Networks (https://arxiv.org/abs/1710.10903)
Publication-quality network visualisations in python
[ICLR 2022] Implementation of paper "Automated Self-Supervised Learning for Graphs"