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Graph Neural Networks Projects

This repository contains implementations of state-of-the art graph machine learning models for recommendations, link prediction and node classification:

  1. Movie recommendations with IGMC [1] (link prediction). Movies are recommended to users using only information from subgraphs.
  2. Link prediction using SEAL [2]. Predict missing links in a citation network by using the SEAL framework.
  3. Node classification using HGT [3]. Predict node properties in a heterogeneous graph.

Installation requirements

ogb>=1.3.0
torch>=1.11.0
pytorch-lightning>=1.2.0
torch-geometric==master (pip install git+https://github.com/rusty1s/pytorch_geometric.git)

Other required python libraries include: numpy, scipy, tqdm etc. These are found in requirements.txt.

Performance

Recommendations (link prediction)

Model Dataset Valid RMSE Test RMSE #Parameters
IGMC MovieLens 52.67 52.73 49k

Node classification

Model Dataset Valid Accuracy (%) Test Accuracy (%) #Parameters
HGT Cora 92.3 - 19M

Link prediction

Model Dataset Valid AP / AUROC Test AP / AUROC #Parameters
HGT Cora 92.3 - 19M
SEAL Cora 94.3 92.6 96K

References

[1] M. Zhang and Y. Chen: Inductive Matrix Completion Based On Graph Neural Networks

[2] M. Zhang and Y. Chen: Link Prediction Based on Graph Neural Networks

[3] Z. Hu et al.: Heterogeneous Graph Transformer