Implementation for DCP-DEC model.
Paper: Deep Embedded Clustering with Distribution Consistency Preservation for Attributed Networks
This paper has been accepted by Pattern Recognition.
Due to the limitation of file size, we give some examples of the real-word datasets (ACM and USPS) and the generated artificial attributed networks (μ=0.6 and μ=0.8 with a noise ratio of 20%).
Here we provide an implementation in PyTorch, along with execution examples on real and artificial (with μ=0.6 & noise ratio is 20%) attributed networks. The repository is organized as follows:
runDCP.py: main process to train the model for real-world networks.
runDCP_lfr.py: train the model for LFR artificial attributed network.
utils.py: process the dataset before passing to the network.
utils_lfr.py: process the LFR artificial attributed network.
model.py: define the autoencoder and graph autoencoder.
evaluation.py: evaluation indicators to verify the performance of the model.
pretrain.py: pretrain the autoencoder and graph autoencoder to get node representations for initializing.
Note that for different datasets, we should change configures of the model to get the best performance.
Example:
python runDCP.py --name acm --alpha 0.1 --beta 0.8 --gamma 0.4 --rae 40 --epochs 500
python runDCP_lfr.py --name lfr100060 --alpha 0.1 --beta 0.01 --gamma 0.5
| Dataset | AE-enc | GAE-enc | Epoch | Lr | alpha | beta | gamma | rae |
|---|---|---|---|---|---|---|---|---|
| Citeseer | 256-64 | 256-64 | 200 | 0.001 | 0.75 | 0.1 | 1 | 1 |
| Cora | 256-64 | 256-64 | 200 | 0.001 | 0.15 | 0.25 | 0.7 | 40 |
| PubMed | 1024-512-256-64 | 256-64 | 200 | 0.001 | 0.1 | 0.05 | 0.8 | 10 |
| ACM | 512-64 | 512-64 | 500 | 0.001 | 0.1 | 0.8 | 0.4 | 40 |
| DBLP | 512-64 | 512-64 | 500 | 0.001 | 0.05 | 0.85 | 1 | 40 |
| USPS | 1024-512-512-256-64 | 256-64 | 500 | 0.001 | 1 | 0.55 | 0.8 | 40 |
| HHAR | 1024-256-256-256-64 | 256-64 | 500 | 0.001 | 0.9 | 0.45 | 0.7 | 40 |
| Reuters | 1024-512-256-64 | 256-64 | 500 | 0.001 | 0.9 | 0.95 | 0.1 | 10 |
Note that we adjust the weight of reconstruction loss in the AE-based module in our experiments.
The baseline models and the reference codes are as below:
D. Bo, X. Wang, C. Shi, et al. Structural Deep Clustering Network. In WWW, 2020.
--https://github.com/bdy9527/SDCN
W. Tu, S. Zhou, X. Liu, X. Guo, Z. Cai, E. Zhu, and J. Cheng. In AAAI 2021.