Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
TypeDB-ML is the Machine Learning integrations library for TypeDB
PyNeuraLogic lets you use Python to create Differentiable Logic Programs
SimplE Embedding for Link Prediction in Knowledge Graphs
The official implementation of NeurIPS22 spotlight paper "NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification"
Readings for "A Unified View of Relational Deep Learning for Drug Pair Scoring." (IJCAI 2022)
Deep relational learning through differentiable logic programming.
[ICLR 2022] Graph-Relational Domain Adaptation
RelNN is a novel first-order deep neural model for relational learning.
A largely incomplete but hopefully useful list of links to datasets for relational learning and inductive logic programming. No guarantees on availability.
Code and data to the publication "SpikE: spike-based embeddings for multi-relational graph data".
The source code repository for the FactorBase system
Machine learning on knowledge graphs for context-aware security monitoring (data and model)
Official implementation of "Relational Proxies: Emergent Relationships as Fine-Grained Discriminators", NeurIPS 2022.
Python package for fetching and using srlearn-compatible relational datasets.
Beyond Graph Neural Networks with Lifted Relational Neural Networks
srlearn-compatible relational datasets
Julia package for fetching and using srlearn-compatible relational datasets.
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