Missing /CTGCN/data/uci/CTGCN/ctgcn_cores

[jhljx]

Missing /CTGCN/data/uci/CTGCN/ctgcn_cores

[jhljx]

You can first run python3 main.py --config=config/uci.json --task=preprocessing --method=CTGCN-C to generate the ctgcn_cores directory and the k-core adjacent matrices. Then you can use '--task=embedding' to generate node embeddings of ctgcn.

Hope this helps you!

[Nick-Kou]

Thank you! My last question is how do you think the CTGCN-S or CTGCN-C models will perform in terms of learning node embeddings in dynamic time-evolving attributed graphs with features? Do you have any suggestions for the hyper parameters? Lastly, how would the feature files be incorporated?

[jhljx]

CTGCN-C might focus on local connective features, while CTGCN-S focus on structural similarity features. You can test both methods on specific tasks.

I test the hyper parameters of CTGCN method in the paper, and I found that this model is robust. So if the memory is limited, you can reduce the k-core number(this is a hyper-parameter) but won't harm the performance. You can also review the parameter sensitivity results in our paper.

If you want to incorporate your own feature files, you need to modify the 'train.py' and 'helper.py'.
In 'train.py' file, I have set a parameter called 'nfeature_folder', you can save all node features at each timestamp in this folder. Then node features will be read from this folder. This folder parameter can be added in the configuration files.

After the above step, you can read node feature through 'get_feature_list' function in the 'helper.py' file. I haven't tested this before, just leave this function for further usage. So you can test it for your own purpose.

[Nick-Kou]

Thank you so much. I really appreciate the assistance and suggestions.