Projects sharing features with Petgraph

AlgorithmsByPython is a reference library and educational repository providing runnable Python implementations of computer science fundamentals. It serves as a comprehensive guide for algorithmic patterns, core data structures, and solutions for competitive programming and technical interview challenges. The project distinguishes itself by offering a wide array of reference implementations, including a dedicated set of solutions for common LeetCode problems. It focuses on translating theoretical computational logic into practical Python code for educational and practical use. The repository

Boost is a collection of portable, high-performance source libraries that extend the C++ standard library. It provides a wide range of reusable components, data structures, and algorithms designed to add capabilities to the base language across different platforms. The project is distinguished by its extensive focus on compile-time template metaprogramming and generic programming. It implements advanced architectural patterns such as policy-based design, concept-based type validation, and the use of SFINAE for conditional template resolution to minimize runtime overhead. The library covers a

This is a collection of standard data structures and algorithmic implementations written in Rust. It provides a suite of specialized libraries designed for competitive programming and systems engineering. The project is organized into distinct toolkits for graph theory, number theory, range queries, and string processing. It includes implementations for computing shortest paths and network flows, performing primality tests and modular arithmetic, and managing associative range queries. The library covers broad computational areas including signal processing via fast Fourier transforms, text

This project is a data structures and algorithms library providing a collection of fifty standard code implementations for managing data and solving common computational problems. It serves as an algorithm implementation reference and study resource for educational use. The codebase covers graph theory implementations for modeling networks and performing searches, as well as string pattern matching libraries for the retrieval of character sequences. It includes a collection of hierarchical data structures, such as binary search trees and priority heaps, and provides optimized solutions for dy

This project is a comprehensive collection of computer science implementations and an algorithm tutorial repository. It serves as a study guide and reference for competitive programming, providing executable code examples that demonstrate fundamental algorithmic problem solving and mathematical computation. The library covers a wide range of specialized domains, including cryptography and security primitives, lossless data compression techniques, and computational geometry for spatial analysis. It also features implementations of machine learning models, linear algebra operations, and formal

This repository provides a collection of fundamental computer science algorithms and data structures implemented in Go. It serves as a technical reference and educational resource, offering reusable modules for common computational tasks including data organization, graph analysis, and numerical operations. The library distinguishes itself through the application of idiomatic Go patterns, utilizing generics for type abstraction and interface-driven polymorphism to ensure compile-time type safety. It emphasizes algorithmic efficiency by employing in-place memory mutation to reduce allocations

LogicStack-LeetCode is a curated repository of solved algorithm problems and data structure implementations, primarily drawn from the LeetCode platform. Its core identity is a structured collection of solutions designed to support technical interview preparation and competitive programming practice, with each solution accompanied by complexity analyses to help engineers understand performance trade-offs. The repository distinguishes itself through its breadth of coverage across fundamental algorithmic patterns and data structures. It includes implementations for array manipulation, string pro

This project is a comprehensive collection of common computer science algorithms and data structures implemented in Swift. It serves as an educational reference and library for studying computational complexity, algorithmic logic, and data structure engineering through practical code examples. The repository provides a wide suite of data structure implementations, including various types of linked lists, heaps, hash tables, and an extensive range of hierarchical trees such as Red-Black, B-Tree, and Splay trees. It also covers diverse sorting and searching techniques, from basic bubble sort to

This project is a comprehensive repository of fundamental computer science algorithms and data structures designed as a reference for academic study, technical interview preparation, and competitive programming. It provides standardized implementations of core computational strategies, serving as an educational resource for developers to master software engineering fundamentals and algorithmic problem-solving. The collection distinguishes itself through a multi-language approach, offering cross-language solutions for complex tasks ranging from graph traversal and dynamic programming to bitwis

Memgraph is an in-memory, distributed graph database designed for high-performance labeled property graph management. It utilizes a Cypher query engine for declarative data retrieval and manipulation, providing a scalable knowledge graph backend that integrates vector search and graph traversals. The system distinguishes itself as a real-time graph analytics platform, employing native C++ and CUDA implementations to execute complex network analysis and dynamic community detection on streaming data. It provides specialized support for AI integration, including GraphRAG capabilities, the constr

Kùzu is an embedded property graph database engine designed for high-performance analytical queries and local data management. It operates as a library within the host application process, utilizing a columnar-based storage architecture and just-in-time query compilation to execute complex graph traversals and pattern matching efficiently. By mapping database files directly into system memory, it ensures data durability and high-speed access while maintaining ACID-compliant transactional integrity. The engine distinguishes itself by integrating vector similarity search and full-text search di

viz-js is a JavaScript rendering library and graph visualization engine that converts Graphviz DOT language descriptions into visual diagrams. It functions as a Graphviz rendering library designed to produce SVG output for web applications. The project utilizes a WebAssembly port of the Graphviz C library to execute layout engines directly in the browser. To maintain interface responsiveness, it processes computationally expensive layout calculations within background worker threads and uses a standardized JSON format for layout serialization. The library provides tools for dynamic graph vis

This project is a comprehensive library of reference implementations for fundamental data structures and algorithms, designed to support technical interview preparation and software engineering assessments. It provides a structured collection of computational techniques for solving complex problems involving arrays, strings, graphs, trees, and mathematical analysis. The library distinguishes itself by offering specialized implementations for advanced topics, including concurrent programming patterns and geometric algorithms. It features thread-safe primitives for managing shared state and tas

This project is a curated reference library of algorithmic patterns, data structure implementations, and system design notes. It serves as a Java algorithmic problem set and a competitive programming guide, providing a collection of solutions for coding challenges from platforms like LeetCode and LintCode. The library is distinguished by its comprehensive set of Java implementations for advanced data structures and algorithmic strategies. It includes detailed references for solving complex problems with accompanying time and space complexity analysis. The project covers a broad surface of co

This repository serves as a comprehensive library for algorithmic problem solving, providing reference implementations for fundamental computer science challenges. It is designed as a resource for technical interview preparation and competitive programming training, focusing on the mastery of common patterns and data structures required for coding assessments. The project distinguishes itself by offering solutions that emphasize idiomatic Python usage and performance optimization. It covers a wide range of algorithmic techniques, including greedy selection, dynamic programming, graph theory,

This project is a collection of reference implementations for algorithms, mathematics, cryptography, compression, and machine learning written in C#. It serves as an educational library providing standard implementations of sorting, searching, and graph theory algorithms. The repository covers a wide range of computational domains, including combinatorial optimization for constraint satisfaction and scheduling, as well as symmetric and classical cryptographic ciphers. It also provides reference code for lossless data compression techniques and fundamental machine learning primitives such as r

go-diagrams is a Go visualization library and DOT language generator used to create system architecture diagrams. It implements a diagrams-as-code approach, allowing users to define system components, infrastructure dependencies, and data flows using Go source code. The library translates these code-based definitions into Graphviz DOT syntax, which is then processed by the Graphviz toolset to render final visual image assets. It supports mapping directional edges to illustrate dependencies and organizing related components into named clusters or subgraphs to represent architectural boundaries

This project is a C++ algorithm implementation library and educational codebase that translates theoretical textbook pseudocode into verified, executable source code. It serves as a collection of reference implementations designed to demonstrate the practical application of classic computer science theories through a structured repository of computational algorithms. The library utilizes template-based generic programming and the C++ Standard Template Library to ensure implementations remain type-safe and flexible across different data types. To ensure correctness, the project includes an aut

Visualize call graph of a Go program using Graphviz

code2flow is a static program flow mapper and source code call graph generator. It analyzes source code to produce visual flow diagrams that map function call relationships and execution paths. The project includes an asynchronous call trace visualizer that follows execution paths through async and await calls to map the logic of asynchronous programs. It also provides a programmable code analysis interface, allowing the call graph generation logic to be integrated into other software applications. The system handles static code analysis by converting source code into abstract syntax trees t

This project serves as a centralized knowledge base and study guide for mastering computer science fundamentals and technical interview preparation. It provides a structured collection of algorithmic implementations, data structure guides, and theoretical references designed to support professional development and problem-solving skills. The repository distinguishes itself through a taxonomy-based organization that maps complex concepts into a hierarchical structure. It standardizes the expression of abstract data structures and algorithms using a consistent programming language, with impleme

Cnn_graph is a graph convolutional network framework and graph signal processing library designed for machine learning research. It provides computational notebooks and code to process and classify graph-structured data by combining node features with an underlying adjacency matrix representation. The framework performs spectral graph convolutions through localized filters and accelerates filtering operations using truncated Chebyshev polynomials to avoid explicit graph Laplacian diagonalization. It includes a graph-structured data pipeline and sparse adjacency representations to handle irreg

This project is a comprehensive suite of Java-based implementations for standard computer science algorithms, data structures, graph analysis, and mathematical computations. It provides a collection of reference implementations for fundamental data containers, including trees, heaps, maps, tries, and lists, alongside common sorting and searching routines. The library includes a specialized suite for graph network analysis, covering shortest paths, minimum spanning trees, and maximum flow. It also provides mathematical utilities for prime testing, modular arithmetic, and Fast Fourier Transform

This project is a comprehensive technical interview preparation resource and computer science interview guide. It serves as an educational reference for developers to study core software engineering fundamentals and common coding patterns required for employment screenings. The repository provides detailed guides and references covering data structures and algorithms, networking and security, operating systems, and web development. It specifically focuses on the implementation and complexity analysis of sorting, searching, and graph algorithms. The material encompasses a wide breadth of comp

Gonum is a numerical computing library for the Go programming language, providing a collection of packages for scientific computing, linear algebra, statistics, and optimization. It functions as a framework for performing complex numerical computations and solving systems of linear equations. The project includes a dedicated graph analysis framework for modeling network graphs and solving connectivity and pathfinding problems. It also provides a statistical analysis toolkit for computing descriptive and inferential statistics and estimating mixture entropy. The library's capability surface c

Pattern is a Python web mining library that functions as an HTML web scraper, a natural language processing toolkit, and a network analysis tool. It provides a mathematical framework for categorizing datasets through a vector space model library. The project enables the extraction of structured data from web services and the creation of searchable web content indexes. It processes unstructured text using sentiment analysis, part-of-speech tagging, and n-gram searching. The library covers machine learning classification through the training of models using perceptron algorithms and support ve

Codeforces-go is a competitive programming algorithm library written in Go, providing a collection of reusable code templates for solving algorithmic problems. It covers core areas including data structures, graph algorithms, dynamic programming, and mathematical computation, with pre-built implementations for segment trees, Fenwick trees, shortest paths, minimum spanning trees, knapsack DP, interval DP, and number theory routines. The library organizes its templates by algorithmic pattern, grouping them into categories such as DP, graph, and math to match common contest question structures.