Open-source alternatives to HypER

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

A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).

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

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

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).

Generate embeddings from large-scale graph-structured data.

Official PyTorch Implementation of SAGPool - ICML 2019

Position-aware Graph Neural Networks

MagNet graph convolutional network

Code for experiments in the AAAI 2016 paper "Holographic Embeddings of Knowledge Graphs"

🤖 A Python library for learning and evaluating knowledge graph embeddings

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.

Embed arbitrary graphs in Hyperbolic space

Knowledge Graph Embeddings including TransE, TransH, TransR and PTransE

Convolutional 2D Knowledge Graph Embeddings resources

Source code for experiments in the papers "Complex Embeddings for Simple Link Prediction" (ICML 2016) and "Knowledge Graph Completion via Complex Tensor Factorization" (JMLR 2017).