awesome-repositories.com
Blog
MCP
awesome-repositories.com

Discover the best open-source repositories with AI-powered search.

ExploreCurated searchesOpen-source alternativesSelf-hosted softwareBlogSitemap
ProjectMCP serverAboutHow we rankPress
LegalPrivacyTerms
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
tkipf avatar

tkipf/gcn

0
View on GitHub↗

Gcn

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 strategies.

The system supports semi-supervised node classification, where labels for unknown nodes are predicted using a small subset of labeled examples and structural connectivity. It also covers graph-level classification by aggregating node information into single vectors for whole-graph categorization.

AI search

Explore more awesome repositories

Describe what you need in plain English — the AI ranks thousands of curated open-source projects by relevance.

Start searching with AI

Features

  • Graph Message Passing Frameworks - Provides frameworks for implementing neural message passing primitives to aggregate node and edge features.
  • Graph - Aggregates neighborhood features using spectral convolutions or Chebyshev polynomial approximations within neural network layers.
  • Spectral Convolutions - Computes node representations by performing a Fourier transform on the graph Laplacian using learned filters.
  • Spectral Graph Convolution Tools - Implements graph convolutions using spectral filters and Chebyshev polynomial approximations for feature aggregation.
  • Chebyshev Polynomial Approximations - Approximates spectral filters using truncated polynomials to avoid expensive eigendecomposition of the graph Laplacian.
  • Node Classification - Predicts labels for unknown nodes in a graph using only a small set of labeled examples.
  • Graph Classification - Assigns single categories to entire graph structures by aggregating local node and edge features.
  • Graph Representation Learning - Creates low-dimensional vector representations of nodes that capture local topology and structural similarity.
  • Multi-Label Classifiers - Predicts labels for individual nodes within a graph structure based on training from a small labeled subset.
  • Embedding Generation - Generates low-dimensional vector representations of nodes based on their structural connectivity within a graph.
  • Graph Batching Optimizations - Provides optimized block-diagonal adjacency matrices to batch multiple graphs of different sizes for GPU processing.
  • Batch - Processes batches of graphs with varying sizes using block-diagonal adjacency matrices to generate labels.
  • Graph Pooling - Implements global pooling layers to reduce node embeddings into a single vector for whole-graph classification.
  • Multi-Graph Processing Frameworks - Handles batches of different graph instances using block-diagonal adjacency matrices and pooling strategies.
  • Adjacency-Based Feature Propagation - Implements feature propagation by iteratively multiplying the adjacency matrix with node feature matrices.
  • Graph Neural Networks - Implementation of semi-supervised classification on graph structures.
7,361 stars·2,011 forks·Python·mit·20 views

Star history

Star history chart for tkipf/gcnStar history chart for tkipf/gcn

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does tkipf/gcn do?

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.

What are the main features of tkipf/gcn?

The main features of tkipf/gcn are: Graph Message Passing Frameworks, Graph, Spectral Convolutions, Spectral Graph Convolution Tools, Chebyshev Polynomial Approximations, Node Classification, Graph Classification, Graph Representation Learning.

Which projects share features with tkipf/gcn?

Projects with overlapping indexed features include: dmlc/dgl — DGL is a Python library for building and training graph neural networks. It functions as a graph message passing… dsgiitr/graph_nets — Graph Nets is a graph neural network library and educational toolkit implemented in PyTorch, providing implementations… mdeff/cnn_graph — Cnn_graph is a graph convolutional network framework and graph signal processing library designed for machine learning… 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… tkipf/pygcn — pygcn is a PyTorch library and framework for implementing graph convolutional networks. It provides tools for… williamleif/graphsage — GraphSAGE is a graph neural network framework designed for inductive representation learning on large-scale graphs. It…

Projects sharing features with Gcn

These projects share indexed features with Gcn. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • dmlc/dgldmlc avatar

    dmlc/dgl

    14,283View on GitHub↗

    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

    Pythondeep-learninggraph-neural-networks
    View on GitHub↗14,283
  • dsgiitr/graph_netsdsgiitr avatar

    dsgiitr/graph_nets

    1,237View on GitHub↗

    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

    Jupyter Notebookchebyshev-polynomialsdeepwalkgraph-attention-networks
    View on GitHub↗1,237
  • mdeff/cnn_graphmdeff avatar

    mdeff/cnn_graph

    1,369View on GitHub↗

    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

    Jupyter Notebookconvolutional-neural-networksdeep-learninggraph-neural-networks
    View on GitHub↗1,369
  • packtpublishing/hands-on-graph-neural-networks-using-pythonPacktPublishing avatar

    PacktPublishing/Hands-On-Graph-Neural-Networks-Using-Python

    1,044View on GitHub↗

    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

    Jupyter Notebook
    View on GitHub↗1,044
Compare all 30 related projects→