Search Results for "graph theory"
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Showing 66 open source projects for "graph theory"
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The Algorithms Python
All Algorithms implemented in Python
...Each implementation is designed with clarity in mind, favoring readability and comprehension over performance optimization. The project covers various domains including mathematics, cryptography, machine learning, sorting, graph theory, and more. With contributions from a large global community, it continually grows and improves through collaboration and peer review. This repository is an ideal reference for students, educators, and developers seeking hands-on experience with algorithmic concepts in Python. -
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The Algorithms - C++ #
Collection of various algorithms in mathematics, machine learning
...The project is part of the broader “The Algorithms” initiative, which maintains algorithm implementations in several programming languages to support education and knowledge sharing. Within the C++ repository, contributors implement algorithms across a wide range of fields including sorting, graph theory, number theory, machine learning, cryptography, and data structures. Each implementation is designed to be readable and well documented so that learners can understand the logic and structure behind each algorithm. The repository functions both as a study resource and as a reference library for developers who want examples of algorithm implementations in C++. ... -
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Algebraic graphs
Algebraic graphs
Alga is a library for algebraic construction and manipulation of graphs in Haskell. See this Haskell Symposium paper and the corresponding talk for the motivation behind the library, the underlying theory and implementation details. We can give semantics to the constructors in terms of the pair (V, E) of graph vertices and edges. Alga can handle graphs comprising millions of vertices and billions of edges in a matter of seconds, which is fast enough for many applications. We believe there is a lot of potential for improving the performance of the library, and this is one of our top priorities. ... -
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Circuitscape.jl
Algorithms from circuit theory to predict connectivity
Circuitscape is an open-source program that uses circuit theory to model connectivity in heterogeneous landscapes. Its most common applications include modeling the movement and gene flow of plants and animals, as well as identifying areas important for connectivity conservation. The new Circuitscape is built entirely in the Julia language, a new programming language for technical computing. Julia is built from the ground up to be fast. As such, this offers a number of advantages over the... -
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KACTL
KTH algorithm competition template library
KACTL (the KTH Algorithmic Contest Template Library) is an extensively curated and high-performance C++ algorithms library created by the competitive programming team at the Royal Institute of Technology (KTH) to serve as a trusted, battle-tested codebase for algorithmic contests, programming competitions, and general algorithm development. The repository aggregates dozens of concise implementations of essential data structures, numerical methods, graph algorithms, string processing tools,... -
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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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Materials Discovery: GNoME
AI discovers 520000 stable inorganic crystal structures for research
Materials Discovery (GNoME) is a large-scale research initiative by Google DeepMind focused on applying graph neural networks to accelerate the discovery of stable inorganic crystal materials. The project centers on Graph Networks for Materials Exploration (GNoME), a message-passing neural network architecture trained on density functional theory (DFT) data to predict material stability and energy formation. Using GNoME, DeepMind identified 381,000 new stable materials, later expanding the dataset to include over 520,000 materials within 1 meV/atom of the convex hull as of August 2024. ... -
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DynamicHMC
Implementation of robust dynamic Hamiltonian Monte Carlo methods
Implementation of robust dynamic Hamiltonian Monte Carlo methods in Julia. In contrast to frameworks that utilize a directed acyclic graph to build a posterior for a Bayesian model from small components, this package requires that you code a log-density function of the posterior in Julia. Derivatives can be provided manually, or using automatic differentiation. Consequently, this package requires that the user is comfortable with the basics of the theory of Bayesian inference, to the extent of coding a (log) posterior density in Julia. ... -
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LeetCode Master
About "Code Thoughts" LeetCode Practice Guide: 200 classic questions
...The repository contains detailed explanations, categorized problem sets, and step-by-step reasoning behind solutions, making it an effective study companion for developers preparing for technical interviews. Problems are grouped by topic—such as arrays, linked lists, dynamic programming, greedy algorithms, and graph theory—so learners can focus on specific areas systematically. Each solution is accompanied by clear commentary to explain the thought process, algorithm design, and complexity analysis. The project is continuously updated and widely used by learners as both a reference and a roadmap to progress from beginner to advanced LeetCode practice. ... -
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PCGExtendedToolkit
PCGEx is a free (libre) Unreal 5 plugin that expands PCG capabilities. It offers a variety of high-performance nodes; with an edge for building relational graphs (Freeform, Delaunay, Voronoi, MST etc), advanced pathfinding; graph theory and much more.
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JGraphT is a free Java class library that provides mathematical graph-theory objects and algorithms. JGraphT supports a rich gallery of graphs and is designed to be powerful, extensible, and easy to use.
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Awesome Network Analysis
A curated list of awesome network analysis resources
awesome-network-analysis is a curated list of resources focused on network and graph analysis, including libraries, frameworks, visualization tools, datasets, and academic papers. It covers multiple programming languages and domains like sociology, biology, and computer science. This repository serves as a central reference for researchers, analysts, and developers working with network data. -
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The Chemistry Development Kit (CDK) is a scientific, LGPL-ed library for bio- and cheminformatics and computational chemistry written in Java. The main home page of the CDK is now at: http://cdk.github.io
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WingSegment
WingSegment: A tool for Analysis and Modeling of Insect Wings
WingSegment is a MATLAB app-designed tool employing a hybrid approach of computer vision and graph theory for precise insect wing image segmentation. WingSegment detects cells, junctions, Pterostigma, and venation patterns, measuring geometric features and generating Voronoi patterns. The tool utilizes region-growing, thinning, and Dijkstra's algorithms for boundary detection, junction identification, and vein path extraction. -
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CXXGraph
Header-Only C++ Library for Graph Manipulation and Algorithms
CXXGraph is a small library, header only, that manages the Graph and it's algorithms in C++. In other words a "Comprehensive C++ Graph Library". -
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slambook
Code for new book about visual SLAM
slambook is the companion code repository for the first edition of 14 Lectures on Visual SLAM: From Theory to Practice. It organizes practical C++ examples by book chapter so readers can compile, run, and modify each concept. The material progresses from Linux, CMake, rigid-body motion, Eigen, Lie groups, and camera models to complete SLAM components. It demonstrates nonlinear optimization with Ceres and g2o, feature-based visual odometry, and direct visual odometry. Later sections cover... -
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RootGraph
Graphic interface with algorithms to compute line and root graphs
Simple and intuitive graphic interface for graph theory with algorithms to compute line and root graphs of a given graph. - Double click to add a new node. - Left click a node and drag to change the position of the node. - Select a node with left click and add an edge with the next node you select. - Right click a node to delete it. Made in C++ using the wxWidgets library. -
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Symja Java Computer Algebra
Symja - Java computer algebra language & symbolic math library
Symja - computer algebra language and Java symbolic math library. Moved to https://github.com/axkr/symja_android_library. The Android App can be found at: https://github.com/axkr/symja-example -
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AminePlatform
Amine is a Multi-Layer Platform for the dev. of Intelligent Systems
Amine is an Artificial Intelligence Multi-Layer Java Open Source Platform dedicated to the development of various kinds of Intelligent Systems and Agents (Knowledge-Based, Ontology-Based, Conceptual Graph -CG- Based, NLP, Reasoning and Learning, Natural Language Processing, etc.). Ontology, KB can be created and manipulated with various processes. CG theory is used as the main knowledge representation language. Amine provides two languages: PROLOG+CG which extends PROLOG with CG and Amine modules, and SYNERGY which is a visual activation/propagation based language. ... -
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BloodHound Legacy
Six Degrees of Domain Admin
BloodHound Legacy is the deprecated open‑source version of the BloodHound Active Directory attack path analysis tool. It uses graph theory to model and visualize privileged relationships in AD, Entra ID, and Azure environments. Security professionals use it to enumerate domain privilege escalation paths, misconfigurations, and attack surfaces in corporate networks -
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BloodHound
Six Degrees of Domain Admin
BloodHound is a single-page Javascript web application, built on top of Linkurious, compiled with Electron, with a Neo4j database fed by a C# data collector. BloodHound uses graph theory to reveal the hidden and often unintended relationships within an Active Directory or Azure environment. Attackers can use BloodHound to easily identify highly complex attack paths that would otherwise be impossible to quickly identify. Defenders can use BloodHound to identify and eliminate those same attack paths. Both blue and red teams can use BloodHound to easily gain a deeper understanding of privilege relationships in an Active Directory or Azure environment. ... -
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Algorithms Math Models
MATLAB implementations of algorithms
...The repository gathers implementations and case studies across many topics commonly used in contest solutions: optimization (linear, integer, goal and nonlinear programming), heuristic and metaheuristic methods (simulated annealing, genetic algorithms, immune algorithms), neural networks and time-series methods, interpolation and regression, graph theory, cellular automata, grey systems, fuzzy models, partial/ordinary differential equations, and multivariate analysis, among others. The codebase is organized into topic folders (e.g., HeuristicAlgorithm, IntegerProgramming, NeuralNetwork, TimeSeries) and includes dozens of worked examples and links to textbook/source materials that the author used to assemble the collection. -
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pyTorch Tutorials
Build your neural network easy and fast
...The project is structured around clear, executable Python scripts and Jupyter notebooks that demonstrate regression, classification, convolutional networks, recurrent networks, autoencoders, and generative adversarial networks, which gives learners practical exposure to real machine learning tasks. Each example explains PyTorch’s dynamic computation graph, optimization techniques, and core abstractions in a way that is accessible and reproducible. Contributors and authors integrate visual and coded examples so readers can see both the theory and the implementation side-by-side. -
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Awesome Explainable Graph Reasoning
A collection of research papers and software related to explainability
A collection of research papers and software related to explainability in graph machine learning. Deep learning methods are achieving ever-increasing performance on many artificial intelligence tasks. A major limitation of deep models is that they are not amenable to interpretability. This limitation can be circumvented by developing post hoc techniques to explain the predictions, giving rise to the area of explainability. Recently, explainability of deep models on images and texts has... -
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GenReg
Generation of regular graphs
Efficient, complete and non-redundant generation of regular graphs as described in M. Meringer: Fast Generation of Regular Graphs and Construction of Cages. Journal of Graph Theory 30, 137-146, 1999.
