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[TNNLS] Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
Hello, I have a question. I noticed that you divided the data into training, testing, and validation sets in the code, but during the training process, I didn't see any...
After carefully reviewing the code, I understand that the subgraphs produced by the random walk with restart algorithm during the training phase can be referred to as positive pairs, with...
Hello, I would like to ask why there is a need to add an additional virtual node before inputting the node's subgraph and features into the model.
in **run.py**, line **261**, why the **added_feat_zero_row** is in the middle of node sequence, according to the implementation of getting **adj** in a batch, this zero should be appended to...
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[TNNLS] Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning