Awesome GitHub RepositoriesGraph Computing Systems

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 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

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

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

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

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

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

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

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