We provide code for the Paper:
Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networks
Chao Li, Hao Xu, Kun He
[AAAI 2023]
Our main contributions are as follows:
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To our knowledge, PMMM is the first NAS method to search multigraph as the architecture. We propose a new concept of meta multigraph to guide GNNs to propagate messages for NAS on HINs.
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We propose the first stable differentiable architecture search algorithm on HINs, called partial message search, which can consistently discover promising architectures that outperform hand-designed models.
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Thorough experiments are conducted to demonstrate the effectiveness, stability, and flexibility of our method.
python=3.8
pytorch==1.6.0 with CUDA support (by default our model is trained on GPU)
numpy==1.19.1
scipy==1.5.2
pandas==1.1.1
scikit-learn==0.23.2
For the node classification task, please see README under node_classification, and for the recommendation task, please see README under recommendation.
If you find this code and data useful, please consider citing the original work by authors:
@article{li2022differentiable,
title={Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networks},
author={Li, Chao and Xu, Hao and He, Kun},
journal={arXiv preprint arXiv:2211.14752},
year={2022}
}