Infact,ourexperiments are35 designed todemonstrate thatthevGraph frameworkenables community detection andnode representation learning36 to benefit one other, not to prove that it outperforms all existing studies. Therefore, we decided to choose certain37 representativemethods(i.e.,matrixfactorization-based methods,generativemodels,andK-Meansafternodeembed-38 dings) which help validate this point. We will discuss more studies in the revised draft.(2)AboutchoosingK. In39 practice, when the trueK is not given, we can still chooseK according to the performance on validation set (as in40 [14,36]).

The table ( right) shows 100 epoch results using best lr and wd values found at 50 epochs. ViT's patchify stem differs from the proposed convolutional stem in the type of convolution used and We investigate these factors next. The focus of this paper is studying the large, positive impact of changing ViT's default We use AdamW for all experiments. Figure 7 shows the results. The table ( right) shows 100 epoch results using optimal lr and wd values chosen from the 50 epoch runs.

This large-kernel plus large-stride convolution runs counter to typical design choices of convolutional layers in neural networks.

Influence functions (IF) have been seen as a technique for explaining model predictions through the lens of the training data.

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