SUWAN: A supervised clustering algorithm with attributed networks

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Santos, B (Autor) Outra; Pedro Campos (Autor) FEP

Revista

Título: Intelligent Data Analysis

Vol. 27

Páginas: 423-441

ISSN: 1088-467X

Editora: IOS PRESS

Indexação

ISI Web of Knowledge - 0 Citações

Scopus

Outras Informações

ID Authenticus: P-00Y-C5T

DOI: 10.3233/ida-216436

Abstract (EN): An increasing area of study for economists and sociologists is the varying organizational structures between business networks. The use of network science makes it possible to identify the determinants of the performance of these business networks. In this work we look for the determinants of inter-firm performance. On one hand, a new method of supervised clustering with attributed networks is proposed, SUWAN, with the aim at obtaining class-uniform clusters of the turnover, while minimizing the number of clusters. This method deals with representative-based supervised clustering, where a set of initial representatives is randomly chosen. One of the innovative aspects of SUWAN is that we use a supervised clustering algorithm to attributed networks that can be accomplished through a combination of weights between the matrix of distances of nodes and their attributes when defining the clusters. As a benchmark, we use Subgroup Discovery on attributed network data. Subgroup Discovery focuses on detecting subgroups described by specific patterns that are interesting with respect to some target concept and a set of explaining features. On the other hand, in order to analyze the impact of the network's topology on the group's performance, some network topology measures, and the group total turnover were exploited. The proposed methodologies are applied to an inter-organizational network, the EuroGroups Register, a central register that contains statistical information on business networks from European countries.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 19