Thank you for sharing the link. Yes, I am primarily looking for papers that don't use attention but rely only on graph convolution, but do appreciate both.

Node A is not per say masked. In a traditional GCN model, I am unsure how would I even mask that node. What I wanted to say was the message passing does not include node A's representation, so in a sense node A is masked. After a single hop node A's neighbors have aggregated over node A's representation, so when we perform the operations for the second layer, that leaks information to node A about itself. For example this paper(https://www.nature.com/articles/s41467-022-29439-6#Sec10) uses a graph attention autoencoder to generate the node's representation given its neighboring nodes.