Vertex Classification on Weighted Networks

Pattern Recognition;Vertex Classification;Network models;Methods on Graphs;Applied Mathematics & Statistics

Helm, Hayden S

Johns Hopkins University

关键词: Pattern Recognition;    Vertex Classification;    Network models;    Methods on Graphs;    Applied Mathematics & Statistics;   

Others  :  https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/60169/HELM-THESIS-2018.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: JOHNS HOPKINS DSpace Repository

【 摘 要 】

This paper provides a framework for vertex classification on weighted networks. 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. In this paper we look at two settings for the edge weight distributions and propose classification procedures in both settings. We show the effectiveness of the proposed classifiers by comparing them to pass-to-ranks. Moreover, we discuss and show how our method performs when the edge weights do not encode class membership information.