| Texto completo | |
| Autor(es): |
Messias, Guilherme Henrique
;
Goncalves Botelho, Tiago
;
Iasulaitis, Sylvia
;
Coelho Naldi, Murilo
;
Demetrius Baria Valejo, Alan
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | IEEE ACCESS; v. 13, p. 21-pg., 2025-01-01. |
| Resumo | |
The objective of node clustering is to identify distinct groups of vertices in a graph, ensuring that similar nodes are grouped together while dissimilar ones remain apart. Building on the success of graph neural networks (GNNs) in tasks such as node classification and link prediction, several clustering strategies have been developed. Although these methods are highly effective, many share similar strengths and weaknesses, which are often difficult to discern in the absence of a clear taxonomy. Previous studies have attempted to categorize node clustering algorithms; however, most are not restricted to GNN-based methods and instead encompass a wide variety of graph learning techniques. Furthermore, despite significant progress in community detection, valuable insights from this area remain underexplored in the context of GNN-based clustering. To bridge this gap and provide a structured overview of the field, we conducted a Systematic Literature Review (SLR) on node clustering in homogeneous networks using GNNs. A total of 84 articles were reviewed based on 20 inclusion and exclusion criteria. From this analysis, we propose a comprehensive taxonomy that integrates both deep clustering and node embedding models. The findings reveal key research gaps, including the absence of transformer-based models, the high computational cost of some approaches, and dataset bias arising from the over-reliance on widely used benchmarks. The proposed taxonomy offers a clearer framework for understanding existing methodologies and guiding future research directions in GNN-based node clustering. (AU) | |
| 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 |