Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
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Updated
Apr 25, 2024 - Python
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"
Implement the node2vec algorithm using Python
Embedding graphs in symmetric spaces
Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)
The SEMB library is an easy-to-use tool for getting and evaluating structural node embeddings in graphs.
GyroSPD: Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices
Vectorizing knowledge bases for entity linking
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search
An implementation of vdist2vec model in paper A Learning Based Approach to Predict Shortest-Path Distances
Reconstructed GRU, used to process the graph sequence.
✨ Implementation of Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning with pytorch and PyG
Code for the Big Data 2019 Paper - Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions
Social trust Network Embedding (ICDM 2019)
learning GNNs
Methods for embedding the structure of graphs into node features.
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