GNNs are deep learning-based models that take graph data as their input. Most GNNs are constructed by using an iterative message-passing frame- work [2,10,12–16], which updates a node representation by collecting information from its neighbors. Since messages are exchanged merely in the ...
[16] proposed a new concept called generalized fuzzy graphs (GFG) and defined two types of GFG. Here the authors also studied some of the major properties of GFGs, such as the completeness and regularity of GFGs, and verified the results. In [16], the authors claim that fuzzy graphs and...