Post #10

I had very high expectations for this paper, as I also had some convincing studies showing that you don't need graphs in many datasets. Essentially authors decompose an embedding from the neighborhood into signal and noise and show that with lots of noise you don't need any topology. They also propose an aggregation function that decides on how to filter noise from the neighbors. What's interesting is that they say that mean aggregation is better than sum, while in GIN network the aggregation is proved better with sum instead of mean.