# Relational Graph Neural Network Libraries

> Search results for `graph neural network library for relational data` on awesome-repositories.com. 117 total matches; showing the first 50.

Explore on the web: https://awesome-repositories.com/q/graph-neural-network-library-for-relational-data

**Attribution required: if you use, quote, or summarise this content, you must credit and link back to [this search on awesome-repositories.com](https://awesome-repositories.com/q/graph-neural-network-library-for-relational-data).**

## Results

- [android/ndk-samples](https://awesome-repositories.com/repository/android-ndk-samples.md) (10,513 ⭐) — The Android NDK samples provide a comprehensive collection of code examples demonstrating how to integrate C and C++ native code into Android applications. This repository serves as a practical guide for developers utilizing the Android Native Development Kit to implement performance-critical application components that require direct hardware access and low-level system interaction.

The project highlights the use of the Java Native Interface to bridge managed code with native modules, enabling cross-language function calls and efficient data exchange. It demonstrates how to manage native act
- [dask/dask](https://awesome-repositories.com/repository/dask-dask.md) (13,746 ⭐) — Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows from single machines to large clusters. It functions as a cluster resource manager that orchestrates computational logic by representing tasks and their dependencies as directed acyclic graphs. This architecture allows the system to automate the distribution of workloads across available hardware while managing complex execution requirements.

The project distinguishes itself through a lazy evaluation engine that defers data operations until they are explicitly requested, enabl
- [gorgonia/gorgonia](https://awesome-repositories.com/repository/gorgonia-gorgonia.md) (5,919 ⭐) — Gorgonia is a Go library that provides an automatic differentiation engine and a computation graph framework for building and training neural networks. It functions as a CUDA-accelerated tensor library and a SIMD-optimized math library, enabling machine learning workflows entirely within the Go ecosystem.

The library distinguishes itself through a dual-backend architecture that dispatches neural network operations to either a GPU or CPU depending on CUDA availability at runtime. It constructs differentiable directed acyclic graphs of tensor operations, supports reverse-mode automatic gradient
- [gokumohandas/made-with-ml](https://awesome-repositories.com/repository/gokumohandas-made-with-ml.md) (48,343 ⭐) — Made-With-ML is an automated documentation generator and developer experience platform designed to transform source code into structured, searchable reference websites. It functions as a codebase intelligence tool that parses implementation details to provide clear explanations of logic and data requirements.

The system distinguishes itself by leveraging language-level type annotations and structured code comments to generate interface specifications. By utilizing static analysis to extract metadata, it automates the transformation of docstrings into web-ready documentation, ensuring that tec
- [msracver/relation-networks-for-object-detection](https://awesome-repositories.com/repository/msracver-relation-networks-for-object-detection.md) (1,104 ⭐) — Relation Networks for Object Detection
- [google/guava](https://awesome-repositories.com/repository/google-guava.md) (51,473 ⭐) — 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
- [d2l-ai/d2l-en](https://awesome-repositories.com/repository/d2l-ai-d2l-en.md) (29,001 ⭐) — This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation.

The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex
- [eipgen/neural-network-models-for-chemistry](https://awesome-repositories.com/repository/eipgen-neural-network-models-for-chemistry.md) (196 ⭐) — A collection of Neural Network Models for chemistry - Quantum Chemistry Method - Force Field Method - Kernel Methods - Not based on Graph Models - Graph Domain Models - Transformer Domain Models - Universal models - Empirical force field - Semi-Empirical Method - Coarse-Grained Method - Enhanced…
- [networkx/networkx](https://awesome-repositories.com/repository/networkx-networkx.md) (16,641 ⭐) — 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
- [apache/mxnet](https://awesome-repositories.com/repository/apache-mxnet.md) (20,829 ⭐) — This project is a deep learning framework designed for constructing, training, and deploying neural networks across diverse hardware environments. It functions as a high-performance tensor computation library that provides both imperative and symbolic programming interfaces, allowing developers to balance flexible, step-by-step model building with the efficiency of compiled computation graphs.

The framework distinguishes itself through a hybrid execution engine that integrates declarative graph compilation with imperative runtime logic. It supports scalable, distributed training across multip
- [rasmusbergpalm/recurrent-relational-networks](https://awesome-repositories.com/repository/rasmusbergpalm-recurrent-relational-networks.md) (204 ⭐) — Code accompanying the paper Recurrent Relational Networks for Complex Relational Reasoning https://arxiv.org/abs/1711.08028
- [mission-peace/interview](https://awesome-repositories.com/repository/mission-peace-interview.md) (11,306 ⭐) — 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
- [joelgrus/data-science-from-scratch](https://awesome-repositories.com/repository/joelgrus-data-science-from-scratch.md) (9,636 ⭐) — 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
- [zhishengwang/embedded-neural-network](https://awesome-repositories.com/repository/zhishengwang-embedded-neural-network.md) (568 ⭐) — This is a collection of papers aiming at reducing model sizes or the ASIC/FPGA accelerator for Machine Learning, especially deep neural network related applications. (Inspiled by Neural-Networks-on-Silicon) - Tutorials: - Hardware Accelerator: Efficient Processing of Deep Neural Networks. (link)…
- [afshinea/stanford-cs-230-deep-learning](https://awesome-repositories.com/repository/afshinea-stanford-cs-230-deep-learning.md) (7,028 ⭐) — This repository collects illustrated single-page cheat sheets that compress the core topics of Stanford's CS 230 deep learning course into visual reference summaries. The collection covers convolutional neural networks, recurrent neural networks, and practical training techniques, pairing schematic diagrams with mathematical notation to bridge intuition and formal understanding.

The cheat sheets are organized by subject area and link related concepts across topics, such as connecting vanishing gradients to LSTM gates, to reinforce the full deep learning workflow. Practical training advice on
- [dash14/v-network-graph](https://awesome-repositories.com/repository/dash14-v-network-graph.md) (643 ⭐) — An interactive network graph visualization component for Vue 3
- [cp-algorithms/cp-algorithms](https://awesome-repositories.com/repository/cp-algorithms-cp-algorithms.md) (10,805 ⭐) — 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
- [f5/unovis](https://awesome-repositories.com/repository/f5-unovis.md) (2,730 ⭐) — Unovis is a modular SVG and Canvas data visualization library used to build interactive charts, maps, and network graphs. It provides a framework-agnostic set of primitives for creating data dashboards and specialized visualizations.

The library is distinguished by its dedicated toolkits for different visualization domains, including an XY charting library for coordinated plots, a network graph framework for relational data, and a geospatial visualization toolkit for TopoJSON-based mapping.

Its capability surface covers a wide range of data representations, including linear, area, and bar ch
- [fffaraz/awesome-cpp](https://awesome-repositories.com/repository/fffaraz-awesome-cpp.md) (71,817 ⭐) — 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
- [hetelek/neural-network-playground](https://awesome-repositories.com/repository/hetelek-neural-network-playground.md) (379 ⭐) — A neural network Swift playground, with no third party dependencies.
- [directus/directus](https://awesome-repositories.com/repository/directus-directus.md) (36,030 ⭐) — Directus is a headless content platform that functions as a backend service, automatically generating REST and GraphQL APIs by performing introspection on existing SQL database schemas. It serves as a unified data orchestration layer, decoupling content management from frontend delivery while providing a secure, stateless gateway for database transactions.

The platform distinguishes itself through a granular role-based access control engine that enforces security policies at the field level across all API endpoints. It includes a visual, low-code administrative dashboard that allows non-techn
- [eugeneyan/applied-ml](https://awesome-repositories.com/repository/eugeneyan-applied-ml.md) (29,783 ⭐) — This project is a comprehensive, curated knowledge base designed to support the development and maintenance of production-grade machine learning systems. It serves as a centralized repository of industry-standard technical literature, engineering case studies, and research papers, providing a structured reference for practitioners navigating the complexities of modern data science and machine learning engineering.

The resource distinguishes itself through a cross-domain approach that bridges the gap between academic research and practical implementation. By synthesizing proven industry archit
- [boostorg/boost](https://awesome-repositories.com/repository/boostorg-boost.md) (8,493 ⭐) — Boost is a collection of portable, high-performance source libraries that extend the C++ standard library. It provides a wide range of reusable components, data structures, and algorithms designed to add capabilities to the base language across different platforms.

The project is distinguished by its extensive focus on compile-time template metaprogramming and generic programming. It implements advanced architectural patterns such as policy-based design, concept-based type validation, and the use of SFINAE for conditional template resolution to minimize runtime overhead.

The library covers a
- [shenxiaocam/deep-network-embedding-for-graph-representation-learning-in-signed-networks](https://awesome-repositories.com/repository/shenxiaocam-deep-network-embedding-for-graph-representation-learning-in-signed-networks.md) (14 ⭐) — Deep network embedding for graph representation learning in signed networks
- [alexjc/neural-enhance](https://awesome-repositories.com/repository/alexjc-neural-enhance.md) (11,873 ⭐) — Neural Enhance is a deep learning image upscaler and restoration tool designed to increase image resolution and remove blur. It functions as a neural image restoration utility for eliminating noise and JPEG artifacts, and includes a framework for training and tuning custom neural network models against image datasets.

The system utilizes a containerized environment to offload tensor calculations to GPU cores, speeding up neural network inference. It features a batch processing pipeline that queues multiple image files in sequence to maximize hardware throughput.

Capabilities include domain-s
- [pyg-team/pytorch_geometric](https://awesome-repositories.com/repository/pyg-team-pytorch-geometric.md) (23,838 ⭐) — 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
- [daugaard/example-neural-network](https://awesome-repositories.com/repository/daugaard-example-neural-network.md) (10 ⭐) — This example will show how to implement a simple neural network for classification in Ruby using ruby-fann.
- [arangodb/arangodb](https://awesome-repositories.com/repository/arangodb-arangodb.md) (14,091 ⭐) — 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
- [zhaohui-yang/binary-neural-networks](https://awesome-repositories.com/repository/zhaohui-yang-binary-neural-networks.md) (29 ⭐) — Binary neural networks developed by Huawei Noah's Ark Lab
- [dotnet/efcore](https://awesome-repositories.com/repository/dotnet-efcore.md) (14,587 ⭐) — Entity Framework Core is an object-relational mapper that enables developers to interact with database systems using strongly-typed code. It serves as a comprehensive data access framework, providing a unified interface for mapping application objects to relational and non-relational database schemas while managing the lifecycle of data operations through a central context.

The project distinguishes itself through a provider-based architecture that decouples core data access logic from specific database engines, allowing for consistent interaction across diverse storage systems. It features a
- [alexjc/neural-doodle](https://awesome-repositories.com/repository/alexjc-neural-doodle.md) (9,854 ⭐) — Neural Doodle is a collection of neural network tools designed for image upscaling, texture synthesis, and semantic-guided style transfer between visual inputs. It provides a semantic style transfer engine and an example-based image upscaler that increase image resolution by referencing visual details from a target style example.

The project includes a neural texture synthesizer for creating seamless bitmap textures and repeating patterns from a single input style image. It also functions as an image generation tool capable of transforming simple sketches and photos into detailed artwork.

Th
- [kevin-wayne/algs4](https://awesome-repositories.com/repository/kevin-wayne-algs4.md) (7,519 ⭐) — 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
- [vbhavank/siamese-neural-network-for-change-detection](https://awesome-repositories.com/repository/vbhavank-siamese-neural-network-for-change-detection.md) (84 ⭐) — Implementation of "SIAMESE NETWORK WITH MULTI-LEVEL FEATURES FOR PATCH-BASED CHANGE DETECTION IN SATELLITE IMAGERY" [1] Faiz Ur Rahman, Bhavan Kumar Vasu, Jared Van Cor, John Kerekes, Andreas Savakis, "Siamese Network with Multi-level Features for Patch-based Change Detection in Satellite…
- [kodecocodes/swift-algorithm-club](https://awesome-repositories.com/repository/kodecocodes-swift-algorithm-club.md) (29,099 ⭐) — 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
- [flutter/flutter](https://awesome-repositories.com/repository/flutter-flutter.md) (177,056 ⭐) — This project is a multi-platform UI framework designed for building applications that target mobile, web, and desktop environments from a single codebase. It utilizes a declarative paradigm where the user interface is defined as a function of application state, supported by a layered architecture that includes a high-performance rendering engine and a multi-platform compilation model.

The framework provides a comprehensive suite of developer tools, including hot reloading for real-time code injection and diagnostic utilities for monitoring application state and performance. It features a modu
- [ryo-ito/noisy-labels-neural-network](https://awesome-repositories.com/repository/ryo-ito-noisy-labels-neural-network.md) (5 ⭐) — Chainer implementation of Noisy Labels Neural-Network
- [modular-network/ethereum-libraries](https://awesome-repositories.com/repository/modular-network-ethereum-libraries.md) (327 ⭐) — Library contracts for Ethereum
- [gonum/gonum](https://awesome-repositories.com/repository/gonum-gonum.md) (8,316 ⭐) — 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
- [cloudwego/hertz](https://awesome-repositories.com/repository/cloudwego-hertz.md) (7,279 ⭐) — Hertz is a high-performance Go HTTP framework designed for building scalable microservices, RESTful APIs, and AI applications. It functions as a high-performance web server and a communication framework for microservices, utilizing non-blocking I/O and zero-copy memory management to handle high-concurrency traffic.

The project distinguishes itself through a microservices communication toolkit that supports high-efficiency remote procedure calls via gRPC and Thrift protocols. It implements an asynchronous middleware engine based on an onion model, allowing for a pluggable request-response pipe
- [thomaswangweihong/pansharpening-by-convolutional-neural-network](https://awesome-repositories.com/repository/thomaswangweihong-pansharpening-by-convolutional-neural-network.md) (0 ⭐) — Python implementation of Convolutional Neural Network (CNN) proposed in academic paper
- [thealgorithms/c-sharp](https://awesome-repositories.com/repository/thealgorithms-c-sharp.md) (8,049 ⭐) — 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
- [apache/spark](https://awesome-repositories.com/repository/apache-spark.md) (43,467 ⭐) — 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
- [ageron/handson-ml](https://awesome-repositories.com/repository/ageron-handson-ml.md) (25,608 ⭐) — This is a machine learning educational repository consisting of a collection of notebooks and code examples. It provides practical implementations of diverse machine learning algorithms and workflows, ranging from traditional scientific computing to deep learning.

The project features specific implementations of Scikit-Learn models, such as decision trees, random forests, and support vector machines, as well as TensorFlow examples for building neural networks, convolutional layers, and recurrent architectures. It also includes tutorials on reinforcement learning development and the creation o
- [l0sg/relational-rnn-pytorch](https://awesome-repositories.com/repository/l0sg-relational-rnn-pytorch.md) (246 ⭐) — An implementation of DeepMind's Relational Recurrent Neural Networks (NeurIPS 2018) in PyTorch.
- [thomaswangweihong/jointnet-a-common-neural-network-for-road-and-building-extraction](https://awesome-repositories.com/repository/thomaswangweihong-jointnet-a-common-neural-network-for-road-and-building-extraction.md) (9 ⭐) — Python implementation of Convolutional Neural Networks (CNNs) proposed in paper
- [eriklindernoren/ml-from-scratch](https://awesome-repositories.com/repository/eriklindernoren-ml-from-scratch.md) (31,918 ⭐) — This project is an educational toolkit that provides implementations of fundamental machine learning algorithms built from scratch. By avoiding high-level library abstractions, it serves as a pedagogical reference for understanding the mathematical foundations and core mechanics of supervised learning, unsupervised learning, and reinforcement learning models.

The repository distinguishes itself through a modular approach to model construction, allowing users to build custom neural networks by chaining independent functional blocks. It covers a wide range of techniques, including gradient-base
- [dmlc/dgl](https://awesome-repositories.com/repository/dmlc-dgl.md) (14,283 ⭐) — 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
- [inducer/relate](https://awesome-repositories.com/repository/inducer-relate.md) (424 ⭐) — RELATE is an Environment for Learning And TEaching
- [humansignal/label-studio](https://awesome-repositories.com/repository/humansignal-label-studio.md) (27,619 ⭐) — Label Studio is a multi-modal data annotation platform designed to create and manage high-quality training datasets for machine learning. It functions as a self-hosted, containerized environment that supports secure, private deployments, including air-gapped configurations. The platform provides a centralized workspace for labeling diverse media types, such as images, text, audio, and time-series data, to support supervised and reinforcement learning workflows.

The platform distinguishes itself through deep integration with machine learning backends, enabling active learning loops, automated
- [neo4j/neo4j](https://awesome-repositories.com/repository/neo4j-neo4j.md) (15,928 ⭐) — 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