The free version of the LEDA Library of Efficient Data types and Algorithms

Tools and analysis for observing the dynamical state of network systems using sentinel nodes.

Collection of step-by-step playbooks for setting up AI/ML workloads on NVIDIA DGX Spark devices with Blackwell architecture.

Implementation of LCP in C

An Embeddable Graph Database with ISO Graph Query Language Support.

An inky color scheme for prose and code.

Videos, quizzes, code, and slideshows for an Introduction to Graph Neural Networks course

Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnot…

Exact Combinatorial Optimization with Graph Convolutional Neural Networks (NeurIPS 2019)

[ICLR 2023] "On Representing Mixed-Integer Linear Programs by Graph Neural Networks" by Ziang Chen, Jialin Liu, Xinshang Wang, Jianfeng Lu, Wotao Yin.

[ICLR 2023 spotlight] "On Representing Linear Programs by Graph Neural Networks" by Ziang Chen, Jialin Liu, Xinshang Wang, Jianfeng Lu, Wotao Yin.

Metagraph Open Data and Query on AWS

Modelling human health trajectories using generative transformers

This API provides programmatic access to the AlphaGenome model developed by Google DeepMind.

Example code to create high-quality knowledge graphs using entity resolution with Kuzu and Senzing

Hands-On Graph Neural Networks Using Python, published by Packt

Embedded property graph database built for speed. Vector search and full-text search built in. Implements Cypher.

A modular graph-based Retrieval-Augmented Generation (RAG) system

Shared Blogs and Notebooks

A curated list of papers and resources based on "Large Language Models on Graphs: A Comprehensive Survey" (TKDE)

Langchain + Docker + Neo4j + Ollama

Notebooks for Large Language Models (LLMs) Specialization

AI chat for any model.

AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

Code for the paper 'Learning TSP Requires Rethinking Generalization' (CP 2021)

Code for the paper 'An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem' (INFORMS Annual Meeting Session 2019)

GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.