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CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python, Matlab/Octave, Julia or Javascript
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Python Module Dependency graphs
Library for animated data visualizations and data stories.
Transform geospatial relations into graphs for Graph Neural Networks and spatial network analysis
Layout algorithms for visualizing directed acyclic graphs
Easy and extensible benchmarking in Elixir providing you with lots of statistics!
TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.
GraphFrames is a package for Apache Spark which provides DataFrame-based Graphs
An autoML framework & toolkit for machine learning on graphs.
Interactively browse dependency graphs of Nix derivations.
A library for enabling task-based multi-threading. It allows execution of task graphs with arbitrary dependencies.
A D3.js plugin that produces flame graphs from hierarchical data.
Build animated charts in Jupyter Notebook and similar environments with a simple Python syntax.
Interactive viewer for graphs written in Graphviz's dot language.
A toolbox of AI modules written in Swift: Graphs/Trees, Support Vector Machines, Neural Networks, PCA, K-Means, Genetic Algorithms
Flexible command line tool to create graphs from CSV data
Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
Tool to check C++ #include dependencies (dependency graphs created in .dot format)
A thin Ruby wrapper to the Neo4j Rest API
Algorithm and data-structure library for .NET 4.5.2+/Netstandard 2.0+. Algorithmia contains sophisticated algorithms and data-structures like graphs, priority queues, command, undo-redo and more.
Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team.
Visualize package dependencies as XKCD-style tower diagrams. Supports Python, Rust, JavaScript, Ruby, PHP, Java, and Go.
Using Fonts More Easily in R Graphs