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Graph Nets is a graph neural network library and educational toolkit implemented in PyTorch, providing implementations of popular graph representation learning algorithms and research papers. The project covers core graph machine learning tasks including semi-supervised node classification, inductive and unsupervised node embedding generation, and neighborhood feature aggregation. The library supports diverse algorithmic approaches for processing network structures, ranging from shared-parameter graph convolutions and attention-weighted neighborhood aggregation to spectral Chebyshev filtering
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
This repository serves as an educational resource for implementing graph neural networks using Python. It provides a collection of structured code examples and tutorials designed to guide developers through the process of building and training machine learning models that operate on complex, interconnected datasets. The project covers the core mechanics of graph-based deep learning, including message-passing architectures, feature aggregation, and the stacking of convolutional layers. It demonstrates how to represent non-Euclidean data as static graphs and how to manage memory during training
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
GraphEmbedding is a graph network representation library and node embedding framework. It provides a toolkit for transforming complex network nodes into low-dimensional vector spaces, enabling the integration of relational graph data into machine learning workflows.
The main features of shenweichen/graphembedding are: Graph Embedding Generators, Dimensionality Reduction Techniques, Graph Neighborhood Sampling, Skip-Gram Model Architectures, Node Embeddings, Graph Representation Learning, Matrix Factorization Toolkits, Graph Embeddings.
Open-source alternatives to shenweichen/graphembedding include: dsgiitr/graph_nets — Graph Nets is a graph neural network library and educational toolkit implemented in PyTorch, providing implementations… dmlc/dgl — DGL is a Python library for building and training graph neural networks. It functions as a graph message passing… packtpublishing/hands-on-graph-neural-networks-using-python — This repository serves as an educational resource for implementing graph neural networks using Python. It provides a… williamleif/graphsage — GraphSAGE is a graph neural network framework designed for inductive representation learning on large-scale graphs. It… tkipf/pygcn — pygcn is a PyTorch library and framework for implementing graph convolutional networks. It provides tools for… thunlp/openke — OpenKE is a knowledge graph embedding framework designed to transform structured knowledge graphs into low-dimensional…