THUDM / GraphMAE

GraphMAE: Self-Supervised Masked Graph Autoencoders in KDD'22
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How to use for graph classification #52

Closed PeterDeSOM closed 1 year ago

PeterDeSOM commented 1 year ago

So far, all that I've found the method for Graph Clustering is actually for node clustering or not a fully unsupervised learning method. It means that they eventually need their label to train at the downstream task.

For example, it is;

I'm finding a fully unsupervised learning method for Graph-Level Clustering/Classification. Or, I'd like to know how to employ your model in a fully unsupervised learning method to classify lots of graph data that cannot be labeled by human efforts.

Question. Could you show me a little direction to use the embedding from your model for the Graph-Level Classification/Clustering without its labels such as KMeans at the downstream task?

THINK2TRY commented 1 year ago

Hi, @PeterDeSOM Thanks for your attention to our work! Actually, GraphMAE is an unsupervised representation learning method, which pre-trains GNNs and learns node-level and graph-level embeddings without relying on any labeled data. The embeddings can be directly applied to any downstream task like clustering without finetuning.

In our experimental setting, we simply train a linear classifier rather than finetune the GNN to evaluate the quality of the learned embeddings. Graph clustering such as KMeans can also be used for evaluation.