Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org

Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)

Quaternion Graph Neural Networks (ACML 2021) (Pytorch and Tensorflow)

From Random Walks to Transformer for Learning Node Embeddings (ECML-PKDD 2020) (In Pytorch and Tensorflow)

Code for the paper "Fine-Grained Entity Typing in Hyperbolic Space"

Embedding graphs in symmetric spaces

Implement the node2vec algorithm using Python

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

GyroSPD: Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices

The SEMB library is an easy-to-use tool for getting and evaluating structural node embeddings in graphs.

Smooth Variational Graph Embeddings for Efficient Neural Architecture Search

Vectorizing knowledge bases for entity linking

An implementation of vdist2vec model in paper A Learning Based Approach to Predict Shortest-Path Distances

Code for the Big Data 2019 Paper - Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions

Reconstructed GRU, used to process the graph sequence.

✨ Implementation of Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning with pytorch and PyG

Social trust Network Embedding (ICDM 2019)

Implementation and evaluation of an autonomous SSI-enhanced iSHARE framework, enabling decentralized identity management, schema alignment for property matching, and automated generation of alternative verification requests, improving scalability, privacy, and flexibility in data spaces and SSI ecosystems.

learning GNNs