Vertex Classification on Weighted Networks
This work addresses classification in weighted networks for researchers, but it is incremental as it builds on existing models and methods.
The paper tackles vertex classification in weighted networks by extending the K-Block Stochastic Block Model with an edge weight distribution matrix, enabling spectral embedding-based classifiers. It shows effectiveness by comparing to quadratic discriminant analysis, with performance evaluated under varying weight informativeness.
This paper proposes a discrimination technique for vertices in a weighted network. We assume that the edge weights and adjacencies in the network are conditionally independent and that both sources of information encode class membership information. In particular, we introduce a edge weight distribution matrix to the standard K-Block Stochastic Block Model to model weighted networks. This allows us to develop simple yet powerful extensions of classification techniques using the spectral embedding of the unweighted adjacency matrix. We consider two assumptions on the edge weight distributions and propose classification procedures in both settings. We show the effectiveness of the proposed classifiers by comparing them to quadratic discriminant analysis following the spectral embedding of a transformed weighted network. Moreover, we discuss and show how the methods perform when the edge weights do not encode class membership information.