This is a Keras implementation of DFNets for semi-supervised classification of nodes on graphs.
Asiri Wijesinghe, Qing Wang, DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters.
- keras (>= 2.2.2)
- tensorflow (>= 1.9.0)
- sklearn (>= 0.19.1)
- cvxpy (>= 1.0.10)
- networkx (>= 2.2)
We use the same data splitting for each dataset as in Yang et al. Revisiting semi-supervised learning with graph embeddings.
We evaluate our method using 3 different models on Cora, Citeseer, Pubmed, and NELL datasets:
DFNet: A densely connected spectral CNN with feedback-looped filters.DFNet-ATT: A self-attention based densely connected spectral CNN with feedback-looped filters.DF-ATT: A self-attention based CNN model with feedback-looped filters.
- dfnets_layer.py - DFNets spectral CNN layer.
- dfnets_optimizer.py - coefficients optimizer.
- dfnets_conv_op.py - convolution operation with feedback-looped filters.
- utils.py - data preprocessing, data spliting, and etc.
- dfnets_example.ipynb - demo code for dfnets.
Please cite our paper if you use this code in your research work.
@inproceedings{asiri2019dfnets,
title={DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters},
author={Wijesinghe, Asiri and Wang, Qing},
booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
year={2019}
}
MIT License
Please contact me: asiri.wijesinghe@anu.edu.au if you have any questions / submit a Github issue if you find any bugs.