Search Results for "graphs"
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Showing 70 open source projects for "graphs"
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XCharts
A charting and data visualization library for Unity
...Supports 3D column charts, funnel charts, pyramids, dashboards, water level charts, pictographic column charts, Gantt charts, rectangular tree charts and other extended charts. Line graphs such as line graphs, curve graphs, area graphs, and stepped line graphs are supported. -
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plotly.py
The interactive graphing library for Python
plotly.py is a browser-based, open source graphing library for Python that lets you create beautiful, interactive, publication-quality graphs. Built on top of plotly.js, it is a high-level, declarative charting library that ships with more than 30 chart types. Everything from statistical charts and scientific charts, through to maps, 3D graphs and animations, plotly.py lets you create them all. Graphs made with plotly.py can be viewed in Jupyter notebooks, standalone HTML files, or hosted online using Chart Studio Cloud. -
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CasADi
CasADi is a symbolic framework for numeric optimization
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 in C++, Python, or Matlab/Octave. CasADi's backbone is a symbolic framework implementing forward and reverse modes of AD on expression graphs to construct gradients, large-and-sparse Jacobians, and Hessians. ... -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. -
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. ... -
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plotly.js
JavaScript charting library behind Plotly and Dash
...Built on top of d3.js and stack.gl, Plotly.js is a high-level, declarative charting library. plotly.js ships with over 40 chart types, including 3D charts, statistical graphs, and SVG maps. plotly.js is free and open source and you can view the source, report issues or contribute on GitHub. For plotly.js to build with Webpack you will need to install ify-loader@v1.1.0+ and add it to your webpack.config.json. This adds Browserify transform compatibility to Webpack which is necessary for some plotly.js dependencies. ... -
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Symfony Serializer
Handles serializing and deserializing data structures
Symfony Serializer is a PHP component that converts objects into various formats (like JSON, XML, or YAML) and vice versa. It handles data normalization and denormalization, making it ideal for APIs and data transformation tasks. The serializer supports complex object graphs and custom normalization logic, allowing developers to convert data in a structured and efficient manner. -
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ScottPlot
Interactive Plotting Library for .NET
ScottPlot is a free and open-source plotting library for .NET that makes it easy to interactively display large datasets. Line plots, bar charts, pie graphs, scatter plots, and more can be created with just a few lines of code. The ScottPlot Cookbook demonstrates how to create line plots, bar charts, pie graphs, scatter plots, and more with just a few lines of code. The ScottPlot Demo is a click-to-run application that demonstrates mouse-interactive versions of every cookbook recipe. ... -
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Writer Framework
No-code in the front, Python in the back. An open-source framework
...It follows a hybrid approach where user interfaces are created using a drag-and-drop editor while business logic is implemented in Python, allowing teams to balance speed and flexibility without sacrificing control. The framework is particularly focused on AI use cases, enabling developers to integrate large language models, knowledge graphs, and custom machine learning workflows into user-facing applications. Its architecture enforces a clear separation of concerns between frontend and backend, which improves maintainability and scalability as applications grow in complexity. The system is designed to support rapid prototyping, enabling developers to iterate on UI and backend logic independently and deploy changes quickly. -
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DGL
Python package built to ease deep learning on graph
Build your models with PyTorch, TensorFlow or Apache MXNet. Fast and memory-efficient message passing primitives for training Graph Neural Networks. Scale to giant graphs via multi-GPU acceleration and distributed training infrastructure. DGL empowers a variety of domain-specific projects including DGL-KE for learning large-scale knowledge graph embeddings, DGL-LifeSci for bioinformatics and cheminformatics, and many others. We are keen to bringing graphs closer to deep learning researchers. We want to make it easy to implement graph neural networks model family. ... -
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Graph Notebook
Library extending Jupyter notebooks to integrate with Apache TinkerPop
...Using this open-source Python package, you can connect to any graph database that supports the Apache TinkerPop, openCypher or the RDF SPARQL graph models. These databases could be running locally on your desktop or in the cloud. Graph databases can be used to explore a variety of use cases including knowledge graphs and identity graphs. This project includes many examples of Jupyter notebooks. It is recommended to explore them. All of the commands and features supported by graph notebook are explained in detail with examples within the sample notebooks. You can find them here. As this project has evolved, many new features have been added. If you are already familiar with graph-notebook but want a quick summary of new features added, a good place to start is the Air-Routes notebooks in the 02-Visualization folder. -
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Schema.DTS
JSON-LD TypeScript types for Schema vocabulary
...It supports scenarios such as nested objects, graphs of entities, and action types with input/output constraints, which makes it suitable for rich metadata authoring and linked-data workflows. While it is maintained by Google, it is not an officially supported Google product. -
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Plotly Graphing Library for MATLAB
Plotly Graphing Library for MATLAB
Create interactive charts in your web browser with MATLAB® and Plotly. -
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TensorFlow
TensorFlow is an open source library for machine learning
...The platform can be easily deployed on multiple CPUs, GPUs and Google's proprietary chip, the tensor processing unit (TPU). TensorFlow expresses its computations as dataflow graphs, with each node in the graph representing an operation. Nodes take tensors—multidimensional arrays—as input and produce tensors as output. The framework allows for these algorithms to be run in C++ for better performance, while the multiple levels of APIs let the user determine how high or low they wish the level of abstraction to be in the models produced. ... -
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Cytoscape.js
Graph theory library for visualization and analysis
A fully featured graph library written in pure JS. Permissive open source license (MIT) for the core Cytoscape.js library and all first-party extensions. Used in commercial projects and open-source projects in production. Designed for users first, for both frontfacing app usecases and developer usecases. Highly optimized. Compatible with All modern browsers. Legacy browsers with ES5 and canvas support. ES5 and canvas support are required, and feature detection is used for optional... -
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JavaScript Algo and Data Structures
Algorithms and data structures implemented in JavaScript
javascript-algorithms is an open source repository by Oleksii Trekhleb that provides implementations of algorithms and data structures in JavaScript. Each algorithm includes explanations, complexity analysis, and references for further reading, making it both a coding resource and a study guide. The repository covers topics such as sorting, searching, graph algorithms, cryptography, and data structures like linked lists, stacks, and queues. It is designed to help developers understand... -
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VSCode-ANTLR4
ANTLR4 language support for Visual Studio Code
vscode-antlr4 is a Visual Studio Code extension that provides comprehensive support for ANTLR4 grammar development. It enhances the editing experience by offering syntax highlighting, code completion, error checking, and visualization tools, making it easier to design and debug grammars within the VS Code environment. The extension supports various target languages and integrates seamlessly with ANTLR4's toolchain. -
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Compose Destinations
Annotation processing library for type-safe Jetpack Compose navigation
A KSP library that processes annotations and generates code that uses Official Jetpack Compose Navigation under the hood. It hides the complex, non-type-safe and boilerplate code you would have to write otherwise. No need to learn a whole new framework to navigate - most APIs are either the same as with the Jetpack Components or inspired by them. -
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BenchmarkDotNet
Powerful .NET library for benchmarking
BenchmarkDotNet is a powerful .NET library designed for creating accurate and reproducible benchmarks. It handles complexities like warm-up, outlier removal, and statistical analysis, presenting results in a clean, customizable summary format. BenchmarkDotNet has tons of features that are essential in comprehensive performance investigations. Four aspects define the design of these features: simplicity, automation, reliability, and friendliness. A lot of hand-written benchmarks produce wrong... -
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Integrant
Micro-framework for data-driven architecture
Integrant is a minimalistic micro-framework for building applications following a data-driven architecture. It lets you define system components declaratively as configuration data and handles lifecycle actions (init, halt, resume) in dependency order, serving as a modern alternative to Component or Mount. Integrant was built as a reaction to fix some perceived weaknesses with Component. In Component, systems are created programmatically. Constructor functions are used to build records,... -
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sigma.js
A JavaScript library dedicated to graph drawing
Sigma is a JavaScript library dedicated to graph drawing. It makes easy to publish networks on Web pages, and allows developers to integrate network exploration in rich Web applications. Sigma provides a lot of built-in features, such as Canvas and WebGL renderers or mouse and touch support, to make networks manipulation on Web pages smooth and fast for the user. The default configuration of sigma deals with mouse and touch support, refreshing and rescaling when the container's size changes,... -
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Penzai
A JAX research toolkit to build, edit, & visualize neural networks
Penzai, developed by Google DeepMind, is a JAX-based library for representing, visualizing, and manipulating neural network models as functional pytree data structures. It is designed to make machine learning research more interpretable and interactive, particularly for tasks like model surgery, ablation studies, architecture debugging, and interpretability research. Unlike conventional neural network libraries, Penzai exposes the full internal structure of models, enabling fine-grained... -
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Theseus
A library for differentiable nonlinear optimization
Theseus is a library for differentiable nonlinear optimization that lets you embed solvers like Gauss-Newton or Levenberg–Marquardt inside PyTorch models. Problems are expressed as factor graphs with variables on manifolds (e.g., SE(3), SO(3)), so classical robotics and vision tasks—bundle adjustment, pose graph optimization, hand–eye calibration—can be written succinctly and solved efficiently. Because solves are differentiable, you can backpropagate through optimization to learn cost weights, feature extractors, or initialization networks end-to-end. ... -
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frugally-deep
A lightweight header-only library for using Keras (TensorFlow) models
Use Keras models in C++ with ease. A lightweight header-only library for using Keras (TensorFlow) models in C++. Works out-of-the-box also when compiled into a 32-bit executable. (Of course, 64 bit is fine too.) Avoids temporarily allocating (potentially large chunks of) additional RAM during convolutions (by not materializing the im2col input matrix). Utterly ignores even the most powerful GPU in your system and uses only one CPU core per prediction. Quite fast on one CPU core, and you can... -
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fvcore
Collection of common code shared among different research projects
...Its common modules include timers, logging, checkpoints, registry patterns, and configuration helpers that reduce boilerplate in research code. A standout capability is FLOP and activation counting, which analyzes arbitrary PyTorch graphs to report cost by operator and by module for precise profiling. The file I/O layer (PathManager) abstracts local/remote storage so the same code can read from disks, cloud buckets, or HTTP endpoints. Because it is small, stable, and well-tested, fvcore is frequently imported by projects like Detectron2 and PyTorchVideo to avoid duplicating infrastructure and to keep research repos.
