Deep_GCN_Benchmarking
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the use of self.cached in models/GCN.py
Could you please explain the use of self.cached in models/GCN.py? If the setting is transductive and the self.cached is set to 'False' manually, will that influence the performance (e.g. ACC) of the algorithm?
the self.cached prevented the model from recomputing the normalized graph over and over and just memorizing it since the graph is the same in inductive. Without cached, each run might take longer, but I doubt the ACC will suffer.