Overview

ExpressGNN is an implementation of the ExpressGNN model proposed in the paper "Efficient Probabilistic Logic Reasoning with Graph Neural Networks". It is designed to perform probabilistic logic reasoning using Graph Neural Networks (GNN).

Features

Efficient Probabilistic Logic Reasoning: Utilizes Graph Neural Networks for efficient probabilistic logic reasoning.
Support for Kinship-S1 dataset: Includes functionality to start the inference on the Kinship-S1 dataset.
Support for FB15K-237 dataset: Allows users to run ExpressGNN on the FB15K-237 dataset.
Requirements: Requires Python 3.7, PyTorch 1.1, scikit-learn, NetworkX, and tqdm.

Summary

ExpressGNN is a tool that implements the ExpressGNN model for efficient probabilistic logic reasoning with Graph Neural Networks. It supports datasets like Kinship-S1 and FB15K-237 and has requirements of Python 3.7, PyTorch 1.1, scikit-learn, NetworkX, and tqdm. Users can easily install the required dependencies and start using ExpressGNN for their probabilistic logic reasoning tasks.

Expressgnn

ExpressGNN