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

Dieses Projekt ist eine C#-Algorithmenbibliothek und eine Sammlung von Datenstrukturen. Sie dient als Informatik-Referenz und bietet praktische Implementierungen klassischer Sortier-, Such- und Graphentraversierungsmuster. Die Bibliothek enthält ein dediziertes Toolkit für String-Verarbeitung zur Analyse von Textähnlichkeit, Berechnung von Edit-Distanzen und Verwaltung Präfix-basierter Suchen. Zudem bietet sie eine Graphentheorie-Implementierung zur Modellierung von Netzwerkbeziehungen und zur Berechnung kürzester Pfade. Die Codebasis deckt ein breites Spektrum an Funktionen ab, einschließlich der Verwaltung linearer und hierarchischer Sammlungen, Baumdatenmanipulation und -visualisierung sowie der Berechnung mathematischer Zahlenfolgen.

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

Dies ist eine Sammlung von Standard-Datenstrukturen und algorithmischen Implementierungen, die in Rust geschrieben wurden. Sie bietet eine Suite spezialisierter Bibliotheken, die für Competitive Programming und Systemtechnik entwickelt wurden. Das Projekt ist in verschiedene Toolkits für Graphentheorie, Zahlentheorie, Bereichsabfragen und Textverarbeitung unterteilt. Es enthält Implementierungen zur Berechnung kürzester Pfade und Netzwerkflüsse, zur Durchführung von Primzahltests und modularer Arithmetik sowie zur Verwaltung assoziativer Bereichsabfragen. Die Bibliothek deckt breite Rechenbereiche ab, einschließlich Signalverarbeitung mittels Fast-Fourier-Transformationen, Textanalyse unter Verwendung von Suffix-Arrays und Tries sowie Datenorganisation durch Koordinatenkomprimierung und Sortierdienstprogramme. Sie bietet zudem Werkzeuge zum Parsen von Eingabedaten aus Dateien oder Standard-I/O.

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 ist eine umfassende numerische Bibliothek zum Lösen von gewöhnlichen, stochastischen, verzögerten und algebraischen Differentialgleichungen. Sie fungiert als Hochleistungs-Solver-Suite, die wissenschaftliches Machine Learning, probabilistische Programmierung und automatisierte Differenzierung in einem einheitlichen Framework integriert. Durch die Nutzung von Multiple Dispatch und symbolisch-numerischer Integration bietet die Bibliothek eine flexible Umgebung für komplexe mathematische Modellierung und Simulation. Das Projekt zeichnet sich durch seine Fähigkeit aus, traditionelle numerische Analysen mit modernen Machine-Learning-Techniken zu verbinden. Es unterstützt das Training universeller Differentialgleichungen, was es Benutzern ermöglicht, neuronale Netzwerke direkt in wissenschaftliche Simulatoren einzubetten, um unbekannte Dynamiken zu erlernen und gleichzeitig physikalische Einschränkungen beizubehalten. Darüber hinaus bietet die Bibliothek fortschrittliche Sensitivitätsanalysen und Parameter-Schätzungstools, einschließlich Adjoint-Methoden und Bayes'scher Inferenz, die eine effiziente Modellkalibrierung und die automatisierte Entdeckung von bestimmenden Gleichungen aus Daten ermöglichen. Die Plattform bietet umfangreiche Funktionen für High-Performance Computing, einschließlich hardwareunabhängiger Parallelität, die Simulationen über CPUs, GPUs und verteilte Cluster verteilt. Sie integriert eine rigorose Unsicherheitsquantifizierung durch Intervallarithmetik und Monte-Carlo-Propagation, was eine zuverlässige Fehlerschätzung in numerischen Experimenten gewährleistet. Zusätzlich verfügt das System über ausgefeilte Optimierungsroutinen, wie graphbasierte Sparsity-Erkennung und matrixfreie Produktberechnung, um die Leistung in groß angelegten Systemen zu beschleunigen.

Dieses Projekt ist ein Deep-Learning-Framework für die Erkennung und Verfolgung menschlicher Körper-Keypoints in Bildern und Videostreams. Es fungiert sowohl als Echtzeit-Motion-Tracking-System als auch als Machine-Learning-Umgebung für das Training und die Evaluierung von Pose-Estimation-Modellen. Das System nutzt ein Convolutional Neural Network mit zwei Zweigen, um Körperteilpositionen und deren gerichtete Verbindungen gleichzeitig vorherzusagen. Es verwendet mehrstufige Feature-Verfeinerung, um die Genauigkeit der Keypoint-Lokalisierung zu verbessern, und nutzt Greedy-Parsing- sowie Bipartite-Matching-Algorithmen, um erkannte Teile zu individuellen Skeletten zuzuordnen. Um die Leistung während der Live-Videoanalyse aufrechtzuerhalten, führt das Framework parallele Inferenz über Bildregionen hinweg mittels Tensor-basierter Batch-Verarbeitung aus. Über das Echtzeit-Tracking hinaus bietet die Bibliothek Tools für das Training von Modellen auf annotierten Datensätzen und die Berechnung der Mean Average Precision (mAP) gegenüber standardisierten Benchmarks, um die Erkennungsqualität zu verifizieren. Das Repository enthält die notwendigen Komponenten, um den gesamten Lebenszyklus der Pose Estimation zu verwalten, vom anfänglichen Modelltraining bis zur Leistungsvalidierung.