So first, we are going to talk about. relational classification and iterative classification. So let me explain to you, uh,. the ideas, uh, for this part of the lecture. So first, we wanna talk about what is, uh, relation classification,.

then we'll talk about iterative classification, and then last, I'm going to talk about, uh,. belief propagation as I- as I said, uh, earlier. So, uh, probabilistic relational classifier,. the idea is the following. Uh, the class probability, um,.

Y_v of node v is a weighted average of class probabilities of its neighbors, right?. So this means that for labeled nodes we are going to. fix the class label to be the ground-truth label, the label we are given, and then for, um, unlabeled nodes, we are just initialize the belief that, let's say, they are- uh, they have the color green to be, let say, 0.5, so something, uh, uniform. And then nodes are going to update their belief about what color they are about,. based on the- on the colors of the, eh, nodes, uh, in the network, uh, around them.