| Full text | |
| Author(s): |
dos Santos, Nicolas Roque
;
Minatel, Diego
;
Baria Valejo, Alan Demetrius
;
Lopes, Alneu de A.
Total Authors: 4
|
| Document type: | Journal article |
| Source: | PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2023, PT I; v. 14469, p. 16-pg., 2024-01-01. |
| Abstract | |
Text classification is a fundamental task in Text Mining (TM) with applications ranging from spam detection to sentiment analysis. One of the current approaches to this task is Graph Neural Network (GNN), primarily used to deal with complex and unstructured data. However, the scalability of GNNs is a significant challenge when dealing with large-scale graphs. Multilevel optimization is prominent among the methods proposed to tackle the issues that arise in such a scenario. This approach uses a hierarchical coarsening technique to reduce a graph, then applies a target algorithm to the coarsest graph and projects the output back to the original graph. Here, we propose a novel approach for text classification using GNN. We build a bipartite graph from the input corpus and then apply the coarsening technique of the multilevel optimization to generate ten contracted graphs to analyze the GNN's performance, training time, and memory consumption as the graph is gradually reduced. Although we conducted experiments on text classification, we emphasize that the proposed method is not bound to a specific task and, thus, can be generalized to different problems modeled as bipartite graphs. Experiments on datasets from various domains and sizes show that our approach reduces memory consumption and training time without significantly losing performance. (AU) | |
| FAPESP's process: | 21/06210-3 - Urban spaces-aware services via federated learning in intelligent transport systems |
| Grantee: | Geraldo Pereira Rocha Filho |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 22/03090-0 - Analysis of large amounts of political data and complex networks: mining modelling and applications in Computational Political Science |
| Grantee: | Sylvia Iasulaitis |
| Support Opportunities: | Research Grants - Initial Project |
| FAPESP's process: | 22/09091-8 - Criminality, insecurity, and legitimacy: a transdisciplinary approach |
| Grantee: | Luis Gustavo Nonato |
| Support Opportunities: | Research Grants - eScience and Data Science Program - Thematic Grants |
| FAPESP's process: | 20/09835-1 - IARA - Artificial Intelligence in the Remaking of Urban Environments |
| Grantee: | André Carlos Ponce de Leon Ferreira de Carvalho |
| Support Opportunities: | Research Grants - Research Centers in Engineering Program |