Initial Feedback

Overall this looks really good.

• I see that you give the source when clicking on the figures, but you should give the source on the web page (in the caption) so the casual reader can see what is yours and what is others.
• It might be better to move the background into a page on GNNs or CGNNs or SSL for GNN, and then use one as the Jan/Feb page and the other as the March page. Then the background can be explained better as a tutorial introduction (including more words/explanations around the equations). Only one of the pages needs to be finished for the upcoming deadline (but can be referred to - just mark it as an unfinished draft).
• Some of the formulae are difficult to parse if the reader doesn't already know what is going on, eg $Z={\hat {A}}{\text{ReLU}}({\hat {A}}XW_{0})W_{1}$ where it isn't clear when some letters are written together it means matrix multiplication, and sometimes it doesn't. Perhaps use lower case single letter for functions, and reserve upper case for matrices (you need to choose and explain the meaning).
• I didn't see a comparison of the papers. What was the incremental contribution of one over the other? Did one replace the other or are they solving different problems? What can we learn from the two papers than we couldn't learn from each one separately? Perhaps a short comparison should be in the abstract and a more concrete one in the conclusion.

Thanks a lot for the detailed review.

• I added the source for each figure/table that has been used.
• I moved most of the background to another foundation page ([1](https://wiki.ubc.ca/Graph_Neural_Networks)) and added a (Builds On) section to my article. This is currently a work in progress and will be done in the next assignment.
• The equation has now been moved to the foundation page and I have added a few lines [2](https://wiki.ubc.ca/Graph_Neural_Networks#Graph_Convolutional_Networks) to explain how the equation is to be read.
• So the two papers discuss pre-training strategies on different graph-based tasks (node classification and graph classification). Since this was not clear, I have modified the abstract to make it explicit that the two papers solve separate problems, changed the naming of the headers of the two paper sections, and also modified the conclusion. I hope this makes it more clear and easy to understand.