Weighted stochastic block models
There are several approaches to partitioning the nodes into densely connected groups [In this study, we need the variant for weighted networks. In simple networks, each pair of nodes are either connected or not connected in a binary relation; conversely, in a weighted network we can assign values to the edges of the network. In our case we assign the dissimilarity of two students as the weight of the edges (see Since we can compute the dissimilarity of all pairs of students, then all the edges have non-zero value and we obtain a network that is a complete graph. To reduce the computation cost of a complete weighted graph, we remove one third of the total number of edges, those with lowest dissimilarity (see 