Coarsening effects on k-partite network classification

Autor(es)

Althoff, Paulo Eduardo ; Valejo, Alan Demetrius Baria ; Faleiros, Thiago de Paulo

• Tipo de documento: Artigo Científico
• Fonte: APPLIED NETWORK SCIENCE; v. 8, n. 1, p. 21-pg., 2023-12-05.

Resumo

The growing data size poses challenges for storage and computational processing time in semi-supervised models, making their practical application difficult; researchers have explored the use of reduced network versions as a potential solution. Real-world networks contain diverse types of vertices and edges, leading to using k-partite network representation. However, the existing methods primarily reduce uni-partite networks with a single type of vertex and edge. We develop a new coarsening method applicable to the k-partite networks that maintain classification performance. The empirical analysis of hundreds of thousands of synthetically generated networks demonstrates the promise of coarsening techniques in solving large networks' storage and processing problems. The findings indicate that the proposed coarsening algorithm achieved significant improvements in storage efficiency and classification runtime, even with modest reductions in the number of vertices, leading to over one-third savings in storage and twice faster classifications; furthermore, the classification performance metrics exhibited low variation on average. (AU)

• Processo FAPESP: 21/06210-3 - Serviços cientes dos espaços urbanos via federated learning em sistemas de transporte inteligente
• Beneficiário: Geraldo Pereira Rocha Filho
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 22/03090-0 - Análise de grandes volumes de dados políticos e redes complexas: mineração, modelagens e aplicações em Ciência Política Computacional
• Beneficiário: Sylvia Iasulaitis
• Modalidade de apoio: Auxílio à Pesquisa - Projeto Inicial