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19 repository-uri

Awesome GitHub RepositoriesGraph Relationship Modeling

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.

Awesome Graph Relationship Modeling GitHub Repositories

Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
  • adambard/learnxinyminutes-docsAvatar adambard

    adambard/learnxinyminutes-docs

    12,287Vezi pe GitHub↗

    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.

    Markdown
    Vezi pe GitHub↗12,287
  • gonum/gonumAvatar gonum

    gonum/gonum

    8,316Vezi pe GitHub↗

    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.

    Godata-analysisgogolang
    Vezi pe GitHub↗8,316
  • tensorflow/docsAvatar tensorflow

    tensorflow/docs

    6,320Vezi pe GitHub↗

    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.

    Jupyter Notebookdeep-learningdeep-neural-networksdocumentation
    Vezi pe GitHub↗6,320
  • vmware-archive/octantAvatar vmware-archive

    vmware-archive/octant

    6,244Vezi pe GitHub↗

    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.

    Gogogolangkubernetes
    Vezi pe GitHub↗6,244
  • duo-labs/cloudmapperAvatar duo-labs

    duo-labs/cloudmapper

    6,259Vezi pe GitHub↗

    Represents AWS resources and their relationships as a directed graph for visual exploration and security analysis.

    JavaScriptawscytoscapediagram
    Vezi pe GitHub↗6,259
  • aalhour/c-sharp-algorithmsAvatar aalhour

    aalhour/c-sharp-algorithms

    6,159Vezi pe GitHub↗

    Acest proiect este o bibliotecă de algoritmi C# și o colecție de structuri de date. Servește ca referință de informatică oferind implementări practice ale tiparelor clasice de sortare, căutare și traversare a grafurilor. Biblioteca include un set de instrumente dedicat procesării șirurilor pentru analizarea similitudinii textului, calcularea distanțelor de editare și gestionarea căutărilor bazate pe prefix. De asemenea, dispune de o implementare a teoriei grafurilor pentru modelarea relațiilor de rețea și calcularea celor mai scurte căi. Codul sursă acoperă o gamă largă de capabilități, inclusiv gestionarea colecțiilor liniare și ierarhice, manipularea și vizualizarea structurilor de date de tip arbore și calcularea secvențelor numerice matematice.

    Implements nodes and edges to represent network relationships for pathfinding and analysis.

    C#
    Vezi pe GitHub↗6,159
  • teivah/algodeckAvatar teivah

    teivah/algodeck

    5,819Vezi pe GitHub↗

    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.

    HTML
    Vezi pe GitHub↗5,819
  • camel-ai/oasisAvatar camel-ai

    camel-ai/oasis

    4,833Vezi pe GitHub↗

    Oasis is an LLM-powered multi-agent social simulator and research tool designed to study synthetic social phenomena. It functions as a synthetic social network platform, replicating the infrastructure of social sites including user profiles, follow relationships, and content discovery mechanisms to model human-like social behaviors at scale. The framework orchestrates large-scale agent populations, supporting up to one million autonomous agents. It distinguishes itself by translating language model outputs into concrete social actions and external tool executions through a tool-calling orches

    Represents users and their follow relationships as nodes and edges to simulate information propagation.

    Pythonagent-based-frameworkagent-based-simulationai-societies
    Vezi pe GitHub↗4,833
  • typedb/typedbAvatar typedb

    typedb/typedb

    4,353Vezi pe GitHub↗

    TypeDB este o bază de date graf și un sistem de gestionare a cunoștințelor (knowledge graph) puternic tipizat. Servește ca un magazin de date multi-model care unifică structurile relaționale, document și graf într-un singur mediu, funcționând atât ca o bază de date conformă ACID, cât și ca un motor de interogare declarativ. Sistemul se distinge prin utilizarea modelării n-ary hypergraph și a ierarhiilor de tip polimorfice. Utilizează o schemă puternic tipizată pentru a impune reguli structurale și a valida integritatea datelor, permițând inferența polimorfică bazată pe tip și polimorfismul de interfață bazat pe roluri pentru a rezolva automat relațiile complexe în timpul execuției interogărilor. Platforma acoperă o gamă largă de capabilități, inclusiv calcularea relațiilor recursive prin tabling, tranzacții cu izolare de snapshot și regăsirea declarativă a datelor. De asemenea, suportă disponibilitatea ridicată prin replicarea clusterelor bazată pe consens, controlul accesului bazat pe roluri și integrarea cu agenți AI pentru regăsirea datelor structurate. Gestionarea este susținută printr-o interfață de linie de comandă, iar sistemul oferă instrumente pentru vizualizarea schemelor graf și auditarea activității administrative.

    Implements a hypergraph structure where relations connect any number of objects and possess their own attributes.

    Rustdatabaseinferenceknowledge-base
    Vezi pe GitHub↗4,353
  • apache/ageAvatar apache

    apache/age

    4,236Vezi pe GitHub↗

    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.

    Cage-databaseagensgraphanalytics
    Vezi pe GitHub↗4,236
  • memgraph/memgraphAvatar memgraph

    memgraph/memgraph

    4,163Vezi pe GitHub↗

    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.

    C++cyphergraphgraph-algorithms
    Vezi pe GitHub↗4,163
  • kuzudb/kuzuAvatar kuzudb

    kuzudb/kuzu

    3,965Vezi pe GitHub↗

    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.

    C++cypherdatabaseembeddable
    Vezi pe GitHub↗3,965
  • falkordb/falkordbAvatar FalkorDB

    FalkorDB/FalkorDB

    3,437Vezi pe GitHub↗

    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.

    Ccloud-databasedatabasedatabase-as-a-service
    Vezi pe GitHub↗3,437
  • gramps-project/grampsAvatar gramps-project

    gramps-project/gramps

    2,823Vezi pe GitHub↗

    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.

    Pythonfamily-treegenealogygramps
    Vezi pe GitHub↗2,823
  • specterops/bloodhoundAvatar SpecterOps

    SpecterOps/BloodHound

    2,789Vezi pe GitHub↗

    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.

    Go
    Vezi pe GitHub↗2,789
  • likec4/likec4Avatar likec4

    likec4/likec4

    2,723Vezi pe GitHub↗

    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.

    TypeScriptarchitecturearchitecture-as-codec4
    Vezi pe GitHub↗2,723
  • awslabs/diagram-makerAvatar awslabs

    awslabs/diagram-maker

    2,417Vezi pe GitHub↗

    Diagram Maker este o bibliotecă bazată pe web concepută pentru construirea de instrumente interactive de vizualizare a grafurilor și modelare a datelor. Oferă un framework pentru randarea structurilor de noduri și legături, permițând utilizatorilor să creeze medii de editare personalizate unde relațiile complexe de date pot fi vizualizate și manipulate direct în browser. Biblioteca utilizează o arhitectură modulară, bazată pe plugin-uri, care permite dezvoltatorilor să extindă funcționalitatea de editare de bază pentru a îndeplini cerințe specifice fără a altera codul sursă subiacent. Gestionează starea aplicației printr-un magazin centralizat, imuabil, asigurându-se că interacțiunile și actualizările de date rămân consistente în întregul spațiu de lucru. Framework-ul susține o gamă de capabilități vizuale și analitice, inclusiv capacitatea de a adnota metadate pe conexiuni, de a calcula limite spațiale pentru poziționarea precisă a obiectelor și de a aplica teme de culori alternative. Gestionează interacțiunile utilizatorului printr-un sistem bazat pe evenimente care coordonează actualizările între interfața vizuală și modelul de date subiacent.

    Supports modeling and annotating connections between data points to track complex dependencies.

    TypeScriptawscanvascloud
    Vezi pe GitHub↗2,417
  • junh0328/prepare_frontend_interviewAvatar junh0328

    junh0328/prepare_frontend_interview

    1,725Vezi pe GitHub↗

    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.

    JavaScriptfrontendhandbook
    Vezi pe GitHub↗1,725
  • devamoghs/machine-learning-with-pythonAvatar devAmoghS

    devAmoghS/Machine-Learning-with-Python

    1,333Vezi pe GitHub↗

    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.

    Pythonbeginner-friendlydata-sciencedeep-learning
    Vezi pe GitHub↗1,333
  1. Home
  2. Data & Databases
  3. Graph Relationship Modeling

Explorează sub-etichetele

  • Edge Management2 sub-tag-uriThe process of creating and configuring directed relationships between vertices with associated properties. **Distinct from Graph Relationship Modeling:** Focuses on the operational creation of edges, while Graph Relationship Modeling is about the conceptual representation.
  • Graph-Based RegularizationTechniques that incorporate graph structure into model training to capture relationships between data points. **Distinct from Graph Relationship Modeling:** Distinct from Graph Relationship Modeling: focuses on using graph structure as a regularization signal during ML training, not general network analysis.
  • Hypergraph ModelingModeling data using hypergraphs where relations can connect more than two entities. **Distinct from Graph Relationship Modeling:** Extends graph modeling beyond simple binary edges to include n-ary relations.
  • Kubernetes Object Graph ModelsRepresents Kubernetes cluster resources and their relationships as a navigable graph with color-coded status indicators. **Distinct from Graph Relationship Modeling:** Distinct from Graph Relationship Modeling: focuses on Kubernetes-specific resource relationships and status visualization, not general graph modeling.
  • Lead-Lag Network ModelingModeling temporal precedence and influence relationships between financial assets using directed graphs. **Distinct from Graph Relationship Modeling:** Focuses specifically on temporal lead-lag financial relationships rather than general entity graph modeling
  • Multi-Edge TensorsRepresentation of multiple relationships of the same type between entities using tensor mathematics. **Distinct from Graph Relationship Modeling:** Specifically uses tensor-based storage for multi-edges, unlike general relationship modeling.
  • Relationship EstablishmentThe act of creating directed edges between nodes with specific types and metadata. **Distinct from Graph Relationship Modeling:** Distinct from Relationship Modeling: refers to the concrete operation of establishing a connection rather than the architectural modeling of the graph.
  • Relationship Type IdentificationsUtilities for listing and filtering the types of relationships connected to specific nodes. **Distinct from Graph Relationship Modeling:** Distinct from general modeling: specifically focuses on identifying the existing types of relationships connected to nodes.