Awesome GitHub RepositoriesGraph Libraries

This project is a comprehensive, curated directory of high-quality libraries, tools, and educational resources for C and C++ development. It serves as an ecosystem discovery index, helping developers navigate the vast landscape of third-party components, frameworks, and technical documentation available for the language. The collection is distinguished by its focus on high-performance systems programming and technical mastery. It provides deep coverage of specialized domains including SIMD-accelerated data processing, compile-time template metaprogramming, and asynchronous event-driven archit

Guava is a Java standard library extension and utility toolkit that provides optimized data structures, concurrency tools, and core extensions. It serves as a comprehensive set of helpers for Java development, focusing on reducing repetitive boilerplate logic. The project is distinguished by its specialized implementations of immutable collections, which ensure thread safety and data consistency by preventing accidental modification. It also includes a dedicated graph data structure library for modeling and traversing networks of interconnected nodes and edges, alongside advanced collection t

NetworkX is a Python library designed for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. It provides a comprehensive framework for modeling relationships between entities as graphs, directed graphs, or multigraphs, allowing users to attach arbitrary metadata and properties to nodes and edges. The library distinguishes itself through a modular architecture that decouples graph analysis logic from data storage, utilizing nested dictionaries and adjacency lists to manage topology. It features a pluggable backend system that delegates computat

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 comprehensive reference for algorithms and data structures used to solve complex computational problems in competitive programming. It serves as a technical resource for implementing advanced mathematical programming, computational geometry, and graph theory. The repository provides detailed implementation guides for diversifying algorithmic techniques, including top-down and bottom-up dynamic programming optimization, number theory, and linear algebra. It features specific guides for complex tasks such as constructing planar graphs, solving linear Diophantine equations, and

This project is a collection of foundational machine learning algorithms and data science tools implemented in Python. It focuses on building the logic of these tools using basic programming primitives rather than relying on specialized libraries. The implementation covers several core domains, including a linear algebra library for matrix and vector operations, a statistical analysis toolkit for probability and hypothesis testing, and a framework for map-reduce distributed processing. It also includes implementations for natural language processing, graph theory for network analysis, and var

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

algs4 is a Java data structures library and algorithm reference collection designed as the source code for a standard computer science textbook curriculum. It provides a comprehensive suite of fundamental implementations for sorting, searching, and core data organization. The project serves as a graph theory framework, offering tools for representing directed and undirected graphs and performing complex traversals and pathfinding. It also includes a broad sorting algorithm suite and a specialized library of Java data structures, including stacks, queues, priority queues, and symbol tables. I

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

Warp is a Python framework that JIT-compiles Python functions into CUDA kernels for GPU-accelerated parallel computation, with built-in automatic differentiation and multi-framework array interoperability. At its core, it provides a GPU kernel compilation system that enables writing and executing custom GPU kernels directly from Python, while supporting automatic gradient computation through those kernels for integration with machine learning pipelines. The framework also includes tile-based cooperative computing, where thread blocks partition into tiles for shared-memory and tensor-core opera

هذا المشروع عبارة عن مكتبة خوارزميات C# ومجموعة من هياكل البيانات. يعمل كمرجع لعلوم الحاسب يوفر تطبيقات عملية لأنماط الفرز، والبحث، واجتياز الرسوم البيانية الكلاسيكية. تتضمن المكتبة مجموعة أدوات معالجة نصوص مخصصة لتحليل تشابه النصوص، وحساب مسافات التحرير، وإدارة عمليات البحث القائمة على البادئة. كما تتميز بتطبيق لنظرية الرسوم البيانية لنمذجة علاقات الشبكة وحساب أقصر المسارات. يغطي الكود المصدري مجموعة واسعة من القدرات، بما في ذلك إدارة المجموعات الخطية والهرمية، ومعالجة وتصور بيانات الأشجار، وحساب المتتاليات الرقمية الرياضية.

Algodeck is an open-source collection of flash cards designed for reviewing algorithms, data structures, and system design concepts, specifically curated for technical interview preparation. The project organizes knowledge into atomic question-and-answer pairs and incorporates spaced repetition scheduling to optimize long-term memory retention. The flash card catalog covers a broad range of computer science topics, including classic sorting algorithms like quicksort and mergesort, data structure operations for arrays, trees, heaps, tries, and graphs, as well as bit manipulation techniques for

This is a collection of classical algorithms and data structures implemented as a header-only C++ library. It provides a suite of tools for general algorithm implementation, including data structure management, graph theory analysis, and string processing. The library is distinguished by its specialized toolkits for cryptographic hashing and encoding, featuring implementations of MD5, SHA-1, and Base64. It also includes advanced capabilities for high-performance string processing via suffix trees and arrays, as well as computational number theory for primality testing and arbitrary-precision

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

هذه مجموعة من هياكل البيانات القياسية وعمليات التنفيذ الخوارزمية المكتوبة بلغة Rust. توفر مجموعة من المكتبات المتخصصة المصممة للبرمجة التنافسية وهندسة النظم. تم تنظيم المشروع في مجموعات أدوات متميزة لنظرية الرسم البياني، ونظرية الأعداد، واستعلامات النطاق، ومعالجة النصوص. يتضمن عمليات تنفيذ لحساب أقصر المسارات وتدفقات الشبكة، وإجراء اختبارات الأولية والحساب النمطي، وإدارة استعلامات النطاق الترابطية. تغطي المكتبة مجالات حسابية واسعة بما في ذلك معالجة الإشارات عبر تحويلات فوريه السريعة، وتحليل النصوص باستخدام مصفوفات اللاحقة (suffix arrays) و tries، وتنظيم البيانات من خلال ضغط الإحداثيات وأدوات الفرز. كما توفر أدوات لتحليل بيانات الإدخال من الملفات أو الإدخال/الإخراج القياسي.

This project is a Go algorithm implementation library and a reference for data structures. It serves as a collection of solved coding interview problems and an algorithmic pattern collection, providing a reference of over 100 common challenges implemented in Go. The library focuses on specific problem-solving strategies, including sliding windows, two pointers, and dynamic programming. It provides coded examples of standard sorting, searching, and graph traversal techniques to facilitate the study of algorithmic patterns. The repository covers a broad range of capabilities, including array a

DifferentialEquations.jl is a comprehensive numerical library designed for solving ordinary, stochastic, delay, and algebraic differential equations. It functions as a high-performance solver suite that integrates scientific machine learning, probabilistic programming, and automated differentiation into a unified framework. By leveraging multiple dispatch and symbolic-numeric integration, the library provides a flexible environment for complex mathematical modeling and simulation. The project distinguishes itself through its ability to bridge traditional numerical analysis with modern machine

هذا المشروع عبارة عن إطار عمل للتعلم العميق مصمم لاكتشاف وتتبع النقاط الرئيسية لجسم الإنسان في الصور وتدفقات الفيديو. يعمل كنظام تتبع حركة في الوقت الفعلي وبيئة تعلم آلة لتدريب وتقييم نماذج تقدير الوضعية. يستخدم النظام شبكة عصبية تلافيفية ثنائية الفرع للتنبؤ بمواقع أجزاء الجسم واتصالاتها الاتجاهية في وقت واحد. يستخدم النظام تحسين الميزات متعدد المراحل لتحسين دقة تحديد النقاط الرئيسية ويستخدم خوارزميات التحليل الجشع والمطابقة الثنائية لربط الأجزاء المكتشفة في هياكل عظمية فردية. للحفاظ على الأداء أثناء تحليل الفيديو المباشر، ينفذ إطار العمل استدلالاً متوازياً عبر مناطق الصورة باستخدام معالجة الدفعات القائمة على الموتر. بعيداً عن التتبع في الوقت الفعلي، توفر المكتبة أدوات لتدريب النماذج على مجموعات البيانات المشروحة وحساب متوسط الدقة مقابل المعايير القياسية للتحقق من جودة الاكتشاف. يتضمن المستودع المكونات اللازمة لإدارة دورة حياة تقدير الوضعية بالكامل، من تدريب النموذج الأولي إلى التحقق من الأداء.