| Full text | |
| Author(s): |
Althoff, Paulo Eduardo
;
Valejo, Alan Demetrius Baria
;
Faleiros, Thiago de Paulo
Total Authors: 3
|
| Document type: | Journal article |
| Source: | APPLIED NETWORK SCIENCE; v. 8, n. 1, p. 21-pg., 2023-12-05. |
| Abstract | |
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) | |
| 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 |