Search Results for "graphs"
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Showing 1224 open source projects for "graphs"
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Graphs.jl
An optimized graphs package for the Julia programming language
The goal of Graphs.jl is to offer a performant platform for network and graph analysis in Julia, following the example of libraries such as NetworkX in Python. Offers a set of simple, concrete graph implementations – SimpleGraph (for undirected graphs) and SimpleDiGraph (for directed graphs), an API for the development of more sophisticated graph implementations under the AbstractGraph type, and a large collection of graph algorithms with the same requirements as this API. -
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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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amrlib
A python library that makes AMR parsing, generation and visualization
A python library that makes AMR parsing, generation and visualization simple. amrlib is a python module designed to make processing for Abstract Meaning Representation (AMR) simple by providing the following functions. Sentence to Graph (StoG) parsing to create AMR graphs from English sentences. Graph to Sentence (GtoS) generation for turning AMR graphs into English sentences. A QT-based GUI to facilitate the conversion of sentences to graphs and back to sentences. Methods to plot AMR graphs in both the GUI and as library functions. Training and test code for both the StoG and GtoS models. A SpaCy extension that allows direct conversion of SpaCy Docs and Spans to AMR graphs. -
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NetworkX
Network analysis in Python
NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Data structures for graphs, digraphs, and multigraphs. Many standard graph algorithms. Network structure and analysis measures. Generators for classic graphs, random graphs, and synthetic networks. Nodes can be "anything" (e.g., text, images, XML records). Edges can hold arbitrary data (e.g., weights, time-series). Open source 3-clause BSD license. Well tested with over 90% code coverage. ... -
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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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asciigraph
Go package to make lightweight ASCII line graph in command line apps
Go package to make lightweight ASCII line graph in command line apps with no other dependencies. This package also brings a small utility for command-line usage. -
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MultilayerGraphs.jl
Julia package for the creation and analysis of multilayer graphs
MultilayerGraphs.jl is a Julia package for the creation, manipulation and analysis of the structure, dynamics and functions of multilayer graphs. A multilayer graph is a graph consisting of multiple standard subgraphs called layers which can be interconnected through bipartite graphs called interlayers composed of the vertex sets of two different layers and the edges between them. The vertices in each layer represent a single set of nodes, although not all nodes have to be represented in every layer. -
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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. -
Safetica Data Loss Prevention Software for BusinessesSafetica is a cost-effective, easy-to-use Data Loss Prevention (DLP) solution. It performs security audits, prevents sensitive data from leaving your company, and sheds light on what is going on in your organization.
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Joern
Open-source code analysis platform for C/C++/Java/Binary/Javascript
Joern is a platform for analyzing source code, bytecode, and binary executables. It generates code property graphs (CPGs), a graph representation of code for cross-language code analysis. Code property graphs are stored in a custom graph database. This allows code to be mined using search queries formulated in a Scala-based domain-specific query language. Joern is developed with the goal of providing a useful tool for vulnerability discovery and research in static program analysis. -
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GraphNeuralNetworks.jl
Graph Neural Networks in Julia
GraphNeuralNetworks.jl is a graph neural network library written in Julia and based on the deep learning framework Flux.jl. -
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AmpliGraph
Python library for Representation Learning on Knowledge Graphs
Open source library based on TensorFlow that predicts links between concepts in a knowledge graph. AmpliGraph is a suite of neural machine learning models for relational Learning, a branch of machine learning that deals with supervised learning on knowledge graphs. -
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ONNX.jl
Read ONNX graphs in Julia
ONNX.jl is in the process of a total reconstruction and currently supports saving & loading graphs as a Umlaut.Tape. When possible, functions from NNlib or the standard library are used, but no conversion to Flux is implemented yet. See resnet18.jl for a practical example of graph loading. -
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SmokePing
The Active Monitoring System
SmokePing is a latency monitoring tool that measures, visualizes, and alerts on network latency using configurable probes such as ping, fping, or HTTP checks. Developed by Tobi Oetiker, it provides detailed, historical graphs of network performance, making it ideal for diagnosing intermittent issues or verifying SLAs. It uses RRDtool for data storage and graphing. -
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react-native-graph
Beautiful, high-performance Graphs and Charts for React Native
react-native-graph is a Line Graph implementation based on the high-performance 2D graphics rendering engine "Skia". It's used in the Pink Panda Wallet app to power thousands of token graphs every day. Animations are ran using the Skia animation system and are fully natively interpolated to ensure best possible performance. If animated is false, a light-weight implementation of the graph renderer will be used, which is optimal for displaying a lot of graphs in large lists. -
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pprof
pprof is a tool for visualization and analysis of profiling data
pprof is a profiling visualization and analysis tool that ingests profiles in the profile.proto format and generates human-readable and graph-based reports. It supports multiple profile types (CPU, heap, allocations, contention, etc.) and can present data as text tables, call graphs (via Graphviz/dot), flame graphs, and interactive web UIs. The tool helps developers find hot paths, quantify resource usage, and compare profiles across runs to validate performance changes. It is widely used in Go but also has bindings and exporters for other ecosystems, and the repository includes a Go package for reading and writing profiles programmatically. ... -
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SuiteSparseGraphBLAS.jl
Sparse, General Linear Algebra for Graphs
A fast, general sparse linear algebra and graph computation package, based on SuiteSparse:GraphBLAS. -
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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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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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BTOP++
BTOP++ for Windows
...It relies on ANSI escape sequences for its rich graphical interface; as a result, it needs at least Windows 10 version 1607 for proper support in the standard CMD terminal or a custom terminal emulator with ANSI support. The README highlights practical notes such as recommended fonts that include braille symbols (for nicer graphs) and suggests Windows Terminal for users who want the best experience on older versions. -
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Obsidian TikZJax
Render LaTeX and TikZ diagrams in your notes
A plugin for Obsidian that lets you render LaTeX and TikZ diagrams in your notes. You can render graphs, figures, circuits, chemical diagrams, commutative diagrams, and more. -
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Helix Toolkit
Helix Toolkit is a collection of 3D components for .NET
...Adds variety of functionalities/models on the top of internal WPF 3D models (Media3D namespace). dds variety of functionalities/models on the top of internal .NET Core WPF 3D models (Media3D namespace). Custom 3D Engine and XAML/MVVM compatible Scene Graphs based on SharpDX(DirectX 11) for Universal Windows App. Custom 3D Engine and Scene Graphs based on SharpDX(DirectX 11) for netstandard and .NET Core. Custom 3D Engine and XAML/MVVM compatible Scene Graphs based on SharpDX(DirectX 11) for WinUI 3 (Project Reunion - experimental). -
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Linguist
Detect blob languages, suppress generated files and generate graphs
This library is used on GitHub.com to detect blob languages, ignore binary or vendored files, suppress generated files in diffs, and generate language breakdown graphs. Linguist is a Ruby library so you will need a recent version of Ruby installed. There are known problems with the macOS/XCode supplied version of Ruby that causes problems installing some of the dependencies. Accordingly, we highly recommend you install a version of Ruby using Homebrew, rbenv, rvm, ruby-build, asdf or other packaging system, before attempting to install Linguist and the dependencies. ... -
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Dash for Julia
A Julia interface to the Dash ecosystem for creating analytic web apps
Create beautiful, analytic applications in Julia. Built on top of Plotly.js, React and HTTP.jl, Dash ties modern UI elements like dropdowns, sliders, and graphs directly to your analytical Julia code. -
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Dagger.jl
A framework for out-of-core and parallel execution
Dagger.jl is a framework for out-of-core and parallel computing in Julia that allows users to construct and execute dynamic task graphs. It is designed for large-scale, distributed, and memory-efficient computations. Dagger supports lazy evaluation and scheduling across multiple threads or machines, enabling high-performance workflows for data processing, scientific computing, and machine learning.
