Awesome GitHub RepositoriesGraph Processing

This repository is a comprehensive collection of data structures and algorithms implemented in JavaScript, designed primarily as an educational resource for computer science study and technical interview preparation. It provides modular implementations of fundamental programming concepts, allowing developers to explore algorithmic logic and data organization through self-contained, verifiable code examples. The library distinguishes itself by pairing every implementation with formal Big O notation, providing predictable insights into time and space scaling requirements. Each algorithm is stru

The algorithm is a distributed recommendation engine pipeline designed to construct and serve personalized content timelines. It functions as a multi-stage orchestration layer that aggregates candidate content from diverse social graphs and high-dimensional embedding spaces, processing user interaction data to deliver a unified, ranked experience. The system utilizes a high-performance machine learning serving infrastructure to execute deep learning models that predict engagement probabilities in real-time. It distinguishes itself through a hybrid retrieval strategy that combines graph-traver

This project is a cross-platform desktop application designed for creating, editing, and managing structured diagrams and technical workflows. It provides a visual modeling environment that allows users to construct complex charts through a drag-and-drop interface, supporting the documentation of processes, software architectures, and system flows. The application distinguishes itself by utilizing a layered canvas composition that enables independent manipulation of diagram components, paired with a keyboard-driven workflow that minimizes mouse reliance. It employs scalable vector graphics fo

Apache Spark is a unified distributed data processing engine designed for large-scale data analysis and computation graphs. It functions as a distributed machine learning framework, a graph processing system, a real-time stream processor, and a SQL analytics engine. The system enables the execution of distributed SQL querying, large-scale graph analysis, and real-time stream analytics across clusters of machines. It also provides a scalable environment for implementing machine learning algorithms and predictive model development on massive datasets. The engine incorporates relational query e

This project is a comprehensive collection of software design patterns implemented in Python. It serves as a reference for architectural, behavioral, creational, and structural patterns to guide the organization of Python applications. The collection covers behavioral strategies for managing object communication and state, creational techniques for controlling object instantiation, and structural methods for composing classes and objects into flexible hierarchies. It also includes architectural references for system-wide structuring, such as multi-tier architectures and blackboard models. Th

Supermemory is an artificial intelligence memory management platform designed to provide autonomous agents with persistent, long-term knowledge bases. It functions as a centralized repository that synchronizes multimodal data, enabling agents to maintain context and historical information across complex, multi-session workflows. By serving as a knowledge graph engine and vector database orchestrator, the platform ensures that information remains accessible and relevant for automated tasks. The system distinguishes itself through its hybrid indexing approach, which combines vector similarity s

This project is a deep learning library designed for training neural networks on irregular data structures, including graphs, 3D meshes, and point clouds. It functions as an extension to the PyTorch framework, providing specialized layers and kernels that enable the processing of complex, non-Euclidean information. The library distinguishes itself through a geometric deep learning toolkit that manages the unique requirements of graph-based data. It utilizes sparse matrix-based message passing to aggregate information across nodes and employs dynamic computational graph construction to accommo

This project serves as a comprehensive, community-driven directory of high-quality open-source Python libraries and tools for machine learning, data science, and artificial intelligence. It functions as a centralized resource for developers to discover, evaluate, and track the maintenance status of software packages across the entire machine learning ecosystem. The platform distinguishes itself through automated popularity tracking and data-driven content curation, which programmatically validate and rank projects based on community activity and development velocity. By organizing these tools

Neo4j is a native graph database management system designed to store and query highly connected data using a property-graph model. It provides an ACID-compliant transaction engine that ensures data integrity, supported by a distributed cluster architecture that maintains causal consistency across nodes. Users interact with the system through a declarative query language, which allows for complex pattern matching and path traversal without requiring manual traversal logic. The platform distinguishes itself through its hybrid approach to data retrieval, combining traditional graph-based queries

Cayley is a graph database engine designed for storing and querying interconnected data using a quad-based data model. It functions as an RDF quad store, managing information through subjects, predicates, objects, and labels. The system features a modular graph store architecture with pluggable backends, allowing it to swap between in-memory storage and various external persistent databases. It includes a GraphQL-inspired API and a dedicated data visualizer for the interactive exploration of nodes and edges. Query capabilities cover bidirectional path traversal and multi-syntax execution usi

DGL is a Python library for building and training graph neural networks. It functions as a graph message passing framework and a geometric deep learning tool, enabling the development of models that analyze graph-structured data. The library is designed for large-scale graph processing, utilizing distributed training and neighbor sampling to handle datasets with billions of edges. It provides specialized support for heterogeneous graph modeling, allowing for the representation of complex real-world entities with multiple node and edge types. Its capabilities cover a wide range of graph tasks

MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a comprehensive environment for executing, optimizing, and deploying neural network models directly on mobile and resource-constrained edge devices. The framework distinguishes itself through a robust model optimization toolkit that supports quantization, compression, and structural graph manipulation to minimize memory footprint and maximize execution speed. It features a modular architecture that abstracts hardware-specific backends, allowing models to run efficiently across diverse

This project is a multi-model database system designed to store and manage information as documents, graphs, and key-value pairs within a single engine. It functions as a graph database and knowledge graph platform, providing the infrastructure to build, query, and visualize structured data models. By integrating vector search capabilities, the system serves as a vector database that supports retrieval-augmented generation for artificial intelligence applications. The platform distinguishes itself through a unified query language that allows users to perform document lookups, graph traversals

Kratos is a centralized identity and access management server designed to handle user registration, authentication, and profile management. It functions as an identity flow orchestrator, managing the state and security of authentication processes across web, mobile, and command-line interfaces. The system provides a standards-compliant authorization server that issues tokens and manages delegated access for third-party applications and internal services, supporting multi-factor authentication and custom identity schemas to secure user accounts. The project distinguishes itself through a headl

Rete is a framework for building interactive, node-based visual interfaces and dataflow programming environments. It provides a core engine that processes directed graphs, allowing developers to define modular logic where nodes represent operations and connections represent the flow of data or control. By decoupling the graph logic from the user interface, the framework enables the creation of custom visual editors that can be integrated into various frontend component libraries. The project distinguishes itself through a highly extensible, signal-driven architecture that supports complex req

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

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.

Titan هو قاعدة بيانات رسوم بيانية موزعة ومحرك حوسبة مصمم لتخزين والاستعلام عن مجموعات بيانات ضخمة من العقد والحواف المترابطة عبر مجموعات متعددة الأجهزة. يعمل كطبقة تخزين رسوم بيانية قابلة للتوسع ومتجر معاملات، ويوفر إطار عمل لتنفيذ مهام معالجة الرسوم البيانية واسعة النطاق والاجتيازات العميقة. يتميز النظام بخلفية تخزين قابلة للتوصيل، والتي تفصل محرك الرسوم البيانية عن طبقة الاستمرارية المادية. يستخدم تقسيم البيانات بقطع الرأس (vertex-cut) لموازنة أحمال المعالجة ونموذج خاصية عدد العناصر الذي يسمح للخصائص الفردية بتخزين قيم متعددة. تغطي المنصة مجموعة واسعة من القدرات، بما في ذلك فهرسة الرسوم البيانية متعددة النماذج لعمليات البحث الجغرافية والنص الكامل، وإدارة المخطط العالمي لإعادة فهرسة مجموعات البيانات، وعمليات المعاملات المضمونة بواسطة تسجيل الكتابة المسبقة. كما تدمج انتهاء صلاحية العناصر عبر إعدادات وقت البقاء (TTL) ومراقبة أداء النظام لتتبع نشاط الاستعلام وزمن انتقال المعاملات.

AITemplate is an ahead-of-time deep learning compiler that translates PyTorch neural networks into standalone C++ source code. It functions as a PyTorch to C++ compiler and a GPU kernel fusion engine, producing self-contained executable binaries that run inference without requiring a Python interpreter or deep learning framework runtime. The project generates optimized CUDA and HIP C++ code specifically for NVIDIA TensorCores and AMD MatrixCores. It focuses on maximizing throughput for half-precision floating-point operations through a system that combines multiple neural network operators in

FalkorDB is a high-performance graph database management system and vector graph database. It serves as a knowledge graph construction tool and a GraphRAG knowledge store, integrating structured property graphs with vector search to provide grounded context for large language models. The engine is designed as a multi-tenant graph engine, capable of hosting thousands of isolated datasets within a single instance. The system distinguishes itself by using linear algebra for query execution, treating relationship tensors as matrix multiplications to achieve low-latency multi-hop traversals. It ut