Open-source alternatives to GraphEmbedding

GraphSAGE is a graph neural network framework designed for inductive representation learning on large-scale graphs. It functions as an inductive graph embedding tool and neighborhood aggregation engine, enabling the generation of numerical node representations that generalize to previously unseen data. The system distinguishes itself by computing node embeddings through the aggregation of features from local neighborhoods rather than relying on a global lookup table. This approach allows the framework to operate as both a supervised graph classifier for predicting categorical node classes and

pygcn is a PyTorch library and framework for implementing graph convolutional networks. It provides tools for semi-supervised node classification and the generation of node embeddings from graph-structured data. The system converts graph nodes into low-dimensional vectors based on neighborhood patterns and local similarities. It enables the prediction of node labels by leveraging both a small set of labeled examples and the overall graph topology. The library covers relational data analysis and semi-supervised graph learning. It includes computational primitives for message passing, adjacenc

OpenKE is a knowledge graph embedding framework designed to transform structured knowledge graphs into low-dimensional vector representations. It functions as a library for representation learning and a toolset for converting entities and relations into numerical embeddings. The project includes a link prediction engine to evaluate the likelihood of relationships between entities and identify missing facts in large-scale graphs. It provides a dedicated preprocessing tool to map raw entity and relation strings into numerical identifiers for machine learning training. The framework's capabilit

This is a graph convolutional network library designed for performing node and graph classification on graph-structured data. It functions as a framework for generating graph embeddings and implementing spectral convolutional neural networks to predict labels for nodes and entire graph structures. The library provides specialized tools for spectral graph convolutions, utilizing Chebyshev polynomial approximations to perform feature aggregation. It includes a multi-graph processing framework that manages batches of different graph instances through block-diagonal adjacency matrices and pooling

Starspace is a vector embedding framework designed for training high-dimensional representations of text and images. It functions as a machine learning system for neural ranking, text classification, and knowledge graph embedding, mapping different object types into a shared numerical space to facilitate retrieval and prediction tasks. The system includes specialized tools for knowledge graph completion and link prediction by representing entities and their relationships within a multi-relational vector space. It further provides capabilities for semantic content recommendation and large-scal

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

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

This project is a comprehensive library for numerical linear algebra and scientific computing, designed to provide optimized routines for matrix decomposition, statistical modeling, and high-performance data analysis. It serves as both a toolkit for solving complex linear systems and an educational resource for understanding the fundamental algorithms behind matrix factorizations and numerical solvers. The library distinguishes itself through a focus on randomized numerical linear algebra, utilizing probabilistic algorithms and approximate methods to perform dimensionality reduction and matri

KnowledgeGraphCourse is a structured collection of graduate-level academic materials, lecture notes, and a comprehensive curriculum focused on the theory and application of knowledge graphs. It serves as a markdown-based educational resource that provides navigable course modules and study guides. The material covers specialized research on integrating knowledge graphs with large language models to reduce hallucinations. It includes detailed guides on using the SPARQL language for storing large-scale graph datasets and executing optimized queries. The curriculum spans a broad range of capabi

GraphTransformerNetworks is a graph neural network framework implemented in PyTorch for learning structural representations and performing classification tasks on complex heterogeneous graphs and relational networks. The project provides automated preprocessing pipelines to transform raw graph datasets into standardized formats, alongside model training, forward passes, and gradient backpropagation executed through dynamic tensor operations. The architecture incorporates self-attention mechanisms applied directly to graph structures to learn contextual representations of nodes and edges ac

This repository is a Chinese translation of The Art of Linear Algebra, a visual educational resource that makes abstract linear algebra concepts concrete through clear graphical diagrams. Its core approach replaces symbolic derivations with intuitive illustrations of vector and matrix operations, matrix factorizations, and eigenvalue properties, helping learners see how matrices work from multiple perspectives. The guide distinguishes itself by teaching matrix factorizations—such as LU, QR, eigenvalue decomposition, and singular value decomposition—through a family of decomposition techniqu

This is a comprehensive educational curriculum designed to teach machine learning fundamentals using the Python programming language. It provides a structured course covering the implementation and theory of supervised learning, unsupervised learning, and deep learning. The curriculum is delivered through interactive notebooks that combine executable code with technical tutorials. It includes dedicated guides for building neural network architectures, implementing classification and regression models, and utilizing clustering techniques for pattern discovery in unlabeled data. The materials

This project is an educational resource providing practical code examples and implementations of machine learning algorithms using the Python language. It serves as a guide for constructing predictive pipelines, clustering models, and dimensionality reduction within the Scikit-Learn ecosystem. The repository includes comprehensive demonstrations for supervised and unsupervised learning, as well as detailed examples for implementing neural networks and deep architectures. It also provides practical guidance on exporting model parameters to JSON and wrapping trained models in web APIs for produ

This project is an interactive data science environment that combines code execution, rich media visualization, and narrative documentation into a persistent, browser-based platform. It serves as a comprehensive educational resource for scientific computing, providing a framework for iterative data analysis and machine learning prototyping. The environment is distinguished by its focus on high-performance numerical computing, utilizing vectorized array operations and memory-mapped data structures to handle large-scale computations efficiently. It features a unified estimator interface that st

This is a collection of classical algorithms and data structures implemented as a header-only C++ library. It provides a suite of tools for general algorithm implementation, including data structure management, graph theory analysis, and string processing. The library is distinguished by its specialized toolkits for cryptographic hashing and encoding, featuring implementations of MD5, SHA-1, and Base64. It also includes advanced capabilities for high-performance string processing via suffix trees and arrays, as well as computational number theory for primality testing and arbitrary-precision

This project is a Python data science curriculum and programming tutorial collection. It provides a structured set of educational notebooks and scripts designed to teach data analysis, machine learning, and deep learning. The repository serves as a learning path for building and tuning predictive models, including regression, decision trees, and neural networks. It includes a data visualization guide for creating financial time-series plots and a multiprocessing reference for implementing parallel task execution and shared memory synchronization. The curriculum covers broader capability area

This repository provides a comprehensive educational framework for mastering machine learning and deep learning through a structured curriculum. It integrates theoretical mathematical foundations—including calculus, probability, and linear algebra—with hands-on laboratory implementations that require learners to build algorithms and neural network architectures from scratch. The project distinguishes itself by emphasizing first-principles development, ensuring that students understand the underlying mechanics of backpropagation, layer-wise computation, and model optimization. It covers a broa

Smile is a comprehensive JVM machine learning library and statistical computing toolkit. It provides a suite of algorithms for classification, regression, and clustering, implemented natively for Java, Scala, and Kotlin. The project also functions as a deep learning framework, a natural language processing library, and an inference engine for large language models. The library distinguishes itself through GPU acceleration via LibTorch bindings and support for the ONNX model interchange format. It includes specialized capabilities for large language model inference, featuring Byte-Pair Encodin

Dimensionality reduction alo codes is a Python collection of implementations for classic data reduction algorithms, functioning as a machine learning feature extraction tool. It transforms complex high-dimensional datasets into lower-dimensional spaces through techniques that retain maximum variance, maximize class separability, and translate proximities between data points into spatial distances. The library includes capabilities for eigenvalue variance maximization, linear discriminant projection, nonlinear neighborhood preserving embedding, singular value matrix decomposition, and statist

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

Cnn_graph is a graph convolutional network framework and graph signal processing library designed for machine learning research. It provides computational notebooks and code to process and classify graph-structured data by combining node features with an underlying adjacency matrix representation. The framework performs spectral graph convolutions through localized filters and accelerates filtering operations using truncated Chebyshev polynomials to avoid explicit graph Laplacian diagonalization. It includes a graph-structured data pipeline and sparse adjacency representations to handle irreg

PRMLT provides self-contained MATLAB implementations of every algorithm from the Pattern Recognition and Machine Learning textbook by Christopher Bishop. The code reproduces the book's exact formulas and notation, making each implementation directly traceable to the source material for educational verification and study. The implementations cover the full range of core machine learning methods from the textbook, including classification, clustering, regression, density estimation, and neural network algorithms. Each module is self-contained with heavy comments, and the code uses compact, vect

PyTorch Geometric is a library for building and training machine learning models on graph-structured data. It provides a framework for developing graph neural networks, including a specialized system for implementing node-to-node information exchange via customizable message passing, aggregation, and update functions. The library includes a sparse data processing toolkit that utilizes accelerated CPU and CUDA kernels to perform efficient reductions on large sparse datasets. It supports the creation of specialized architectures for structured data such as 3D meshes and point clouds. The proje

Linear-Algebra-With-Python is an educational resource that provides a structured curriculum for learning linear algebra through computational practice. It serves as a tutorial for data scientists and quantitative analysts, bridging the gap between abstract mathematical theory and practical implementation using Python. The project utilizes a literate programming approach, organizing lecture notes and code examples into interactive documents. By interleaving explanatory text with functional code, it allows users to experiment with mathematical concepts directly within their development environm

This project serves as an educational and practical resource for mastering machine learning workflows using Python. It provides a comprehensive collection of code examples and exercises designed to guide users through the implementation of predictive systems, ranging from fundamental algorithms to deep learning architectures. The repository distinguishes itself by offering a structured approach to both classical machine learning and neural network training. It covers the full lifecycle of model development, including the orchestration of reusable data transformation pipelines, advanced ensemb

A curated list of network embedding techniques.

StellarGraph - Machine Learning on Graphs