19 Repos
Representing entities and their connections as nodes and edges for network analysis.
Distinct from Relationship Modeling: Focuses on general network modeling for pathfinding, unlike database-specific entity-relationship mapping.
Explore 19 awesome GitHub repositories matching data & databases · Graph Relationship Modeling. Refine with filters or upvote what's useful.
This project is a collection of programming language references and syntax cheat sheets designed for rapid developer onboarding. It serves as a library of code-based documentation that uses valid source code files to provide whirlwind tours of various language specifications. The project focuses on programming language learning by providing concise, commented code examples that explain core features and syntax in place. This approach enables developers to quickly grasp language-specific patterns, data types, and execution flow through a consistent reference format. The content covers a broad
Provides examples of establishing labeled connections and directed edges between nodes in a graph.
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
Models relationships between entities as nodes and edges to solve connectivity and pathfinding problems.
This repository is the official documentation for TensorFlow, a machine learning framework. It provides comprehensive guides, tutorials, and API references for building, training, and deploying machine learning models. The documentation covers the full lifecycle of machine learning projects, from constructing data pipelines and building neural networks with high-level APIs to customizing training loops and deploying trained models in production, on edge devices, or in browsers. The documentation includes step-by-step tutorials for a range of tasks, including reinforcement learning, ranking mo
Documents graph neural network tutorials that use graph structure to improve model accuracy.
Highly extensible platform for developers to better understand the complexity of Kubernetes clusters.
Ships a navigable graph model of Kubernetes resources with color-coded status indicators for visual inspection.
Represents AWS resources and their relationships as a directed graph for visual exploration and security analysis.
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.
Implements nodes and edges to represent network relationships for pathfinding and analysis.
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
Describes graph databases for modeling complex many-to-many relationships.
Oasis ist ein LLM-gestützter Multi-Agenten-Sozialsimulator und ein Forschungstool zur Untersuchung synthetischer sozialer Phänomene. Es fungiert als Plattform für synthetische soziale Netzwerke, die die Infrastruktur sozialer Seiten – einschließlich Benutzerprofilen, Follow-Beziehungen und Mechanismen zur Inhaltsentdeckung – repliziert, um menschenähnliches soziales Verhalten in großem Maßstab zu modellieren. Das Framework orchestriert große Agentenpopulationen und unterstützt bis zu eine Million autonome Agenten. Es zeichnet sich dadurch aus, dass es Ausgaben von Sprachmodellen durch einen Tool-Calling-Orchestrator in konkrete soziale Aktionen und externe Tool-Ausführungen übersetzt, während es eine zeitbeschleunigte Simulationsuhr verwendet, um Ereignissequenzen von der Echtzeit zu entkoppeln. Das System deckt breite Funktionsbereiche ab, darunter die Modellierung sozialer Plattformen, graphbasierte Kartierung sozialer Netzwerke und algorithmische Inhaltsempfehlungen. Es bietet spezialisierte Forschungstools für die Modellierung von Informationsverbreitung, die Analyse von Gruppenpolarisierung und Agenten-Interviews, unterstützt durch persistentes Aktivitäts-Logging für retrospektive Datenanalysen. Das Projekt ist in Python implementiert.
Represents users and their follow relationships as nodes and edges to simulate information propagation.
TypeDB ist eine stark typisierte Graphdatenbank und ein Knowledge-Graph-Managementsystem. Es dient als Multi-Modell-Datenspeicher, der relationale, Dokument- und Graphstrukturen in einer einzigen Umgebung vereint und sowohl als ACID-konforme Datenbank als auch als deklarative Abfrage-Engine fungiert. Das System zeichnet sich durch die Verwendung von n-ären Hypergraph-Modellen und polymorphen Typ-Hierarchien aus. Es verwendet ein stark typisiertes Schema, um strukturelle Regeln durchzusetzen und die Datenintegrität zu validieren, was typbasierte polymorphe Inferenz und rollenbasierte Interface-Polymorphie ermöglicht, um komplexe Beziehungen während der Abfrageausführung automatisch aufzulösen. Die Plattform deckt ein breites Spektrum an Funktionen ab, einschließlich der Berechnung rekursiver Beziehungen mittels Tabling, Snapshot-Isolation-Transaktionen und deklarativem Datenabruf. Sie unterstützt zudem Hochverfügbarkeit durch konsensbasierte Cluster-Replikation, rollenbasierte Zugriffskontrolle und die Integration mit KI-Agenten für den strukturierten Datenabruf. Die Verwaltung wird über eine Kommandozeilenschnittstelle unterstützt, und das System bietet Tools zur Visualisierung von Graph-Schemata sowie zur Prüfung administrativer Aktivitäten.
Implements a hypergraph structure where relations connect any number of objects and possess their own attributes.
Apache AGE is a graph database extension for PostgreSQL that adds openCypher graph query capabilities directly within the relational database environment. It functions as a loadable extension that translates Cypher graph traversal queries into SQL expressions, enabling users to run pattern matching and path analysis alongside standard SQL operations within a single database instance. The extension stores labeled, directed property graphs as isolated schemas with internal relational tables for vertices, edges, and labels, preventing cross-graph interference. It supports hybrid query execution
Returns full edge objects with labels and properties from matched graph patterns.
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
The product establishes connections between nodes based on logical rules or shared properties.
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
Establishes connections between node tables by defining source and target labels with optional properties.
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
Represents multiple relationships of the same type between two entities using high-performance tensors.
Gramps is genealogy management software designed to document family trees, ancestral records, and genealogical research. It functions as a family history database that stores complex kinship links and historical records while providing full data versioning. The platform includes a kinship relationship graph for rendering ancestral connections as interactive diagrams and a geographic family tree visualizer that uses spatial data to display the movement and distribution of ancestors. It is built as an extensible platform that supports third-party plugins for custom reports, filters, and interfa
Models ancestral connections as nodes and edges to calculate lineages and generate family network diagrams.
BloodHound is an identity risk management platform and graph-based attack path analyzer used to map identity relationships and permissions in Active Directory. It functions as a security tool for auditing directory services, uncovering unintended privilege relationships, and visualizing sequences of permissions that can lead to domain compromise. The project differentiates itself as a comprehensive adversary emulation framework that coordinates remote agents and executes post-exploitation commands. It includes a reverse proxy for bypassing multi-factor authentication via real-time session hij
Models environments as a graph by ingesting data from identity and device management systems to identify attack paths.
likec4 is an architecture-as-code framework that transforms text-based architecture definitions into interactive diagrams, static websites, and image files. It serves as a system architecture visualizer and C4 model diagram generator, allowing users to define software components, boundaries, and deployment infrastructure using a domain-specific language. The tool distinguishes itself by providing a modeling environment with Language Server Protocol integration for real-time validation and autocomplete. It enables interactive architecture documentation where users can navigate through hierarch
Maintains the system architecture as a resource graph of components and dependencies for hierarchy analysis.
Diagram Maker ist eine webbasierte Bibliothek für den Aufbau interaktiver Diagramm-Visualisierungs- und Datenmodellierungstools. Sie bietet ein Framework für das Rendern von Knoten- und Link-Strukturen, das es Benutzern ermöglicht, benutzerdefinierte Bearbeitungsumgebungen zu erstellen, in denen komplexe Datenbeziehungen direkt im Browser visualisiert und manipuliert werden können. Die Bibliothek nutzt eine modulare, Plugin-gesteuerte Architektur, die es Entwicklern ermöglicht, die Kern-Bearbeitungsfunktionalität an spezifische Anforderungen anzupassen, ohne den zugrunde liegenden Quellcode zu ändern. Sie verwaltet den Anwendungszustand über einen zentralen, unveränderlichen (immutable) Store und stellt sicher, dass Interaktionen und Datenaktualisierungen über den gesamten Arbeitsbereich hinweg konsistent bleiben. Das Framework unterstützt eine Reihe visueller und analytischer Fähigkeiten, einschließlich der Möglichkeit, Metadaten an Verbindungen zu annotieren, räumliche Grenzen für eine präzise Objektpositionierung zu berechnen und alternative Farbthemen anzuwenden. Es handhabt Benutzerinteraktionen über ein ereignisgesteuertes System, das Aktualisierungen zwischen der visuellen Schnittstelle und dem zugrunde liegenden Datenmodell koordiniert.
Supports modeling and annotating connections between data points to track complex dependencies.
This project is a comprehensive technical interview study resource designed to help developers prepare for engineering job assessments. It functions as a structured guide that curates essential computer science fundamentals, web development standards, and programming language concepts into a format optimized for professional evaluation. The repository distinguishes itself by providing strategic guidance on architectural decision-making and professional communication. Beyond simple question-and-answer pairs, it offers frameworks for articulating experience during interviews and suggests profes
Represents entities and their connections as nodes and edges for network analysis.
This repository serves as an educational collection of practical examples and tutorials designed to facilitate the study of machine learning and data science concepts using Python. It provides a structured environment for learning core algorithms and data analysis techniques through hands-on implementation and iterative exploration. The project covers a broad range of analytical capabilities, including predictive modeling for regression, classification, and clustering tasks, as well as network topology analysis for identifying influence patterns in interconnected data. It also incorporates na
Implements graph-based relationship modeling to compute topological metrics and identify influence patterns.