Projects sharing features with Pykeen

A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).

A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).

The official PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf '21)

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)

A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)

A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).

A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).

A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018).

A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).

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

Generate embeddings from large-scale graph-structured data.

PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer

Hypernetwork Knowledge Graph Embeddings

TuckER: Tensor Factorization for Knowledge Graph Completion

Official PyTorch Implementation of SAGPool - ICML 2019

Position-aware Graph Neural Networks

MagNet graph convolutional network

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

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

PyTorch Geometric Signed Directed is a signed/directed graph neural network extension library for PyTorch Geometric. The paper is accepted by LoG 2023.

Official code for the SDM2022 paper -- SSSNET: Semi-Supervised Signed Network Clustering.

Benchmark datasets, data loaders, and evaluators for graph machine learning