| Texto completo | |
| Autor(es): |
de Moraes Junior, Marcelo Isaias
;
Marcacini, Ricardo Marcondes
;
IEEE
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN; v. N/A, p. 8-pg., 2023-01-01. |
| Resumo | |
Network embedding methods map nodes into vector representations, aiming to preserve important properties of relationships between nodes through similarities in a latent vectorspace model. Graph Neural Networks (GNNs) based on aggregate functions have received significant attention among different network embedding methods. In general, the embeddings of a node are recursively generated by aggregating embeddings from neighboring nodes. Aggregation is a crucial step in these methods, and different aggregation functions have been proposed, from simple averaging and max pooling operations to complex functions based on attention mechanisms. However, we note that there is a lack of studies comparing aggregate functions, especially in more practical real-world scenarios involving semi-supervised tasks. This paper introduces a methodology to evaluate different aggregation functions for semi-supervised learning through a model selection strategy guided by a statistical significance analysis framework. We show that Transformers-based aggregation functions are competitive for semi-supervised scenarios and obtain relevant results in different domains. Furthermore, we also discuss scenarios where "less is more", mainly when there are constraints on the availability of computational resources. (AU) | |
| Processo FAPESP: | 22/09091-8 - Criminalidade, insegurança e legitimidade: uma abordagem transdisciplinar |
| Beneficiário: | Luis Gustavo Nonato |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa eScience e Data Science - Temático |
| Processo FAPESP: | 19/25010-5 - Representações semanticamente enriquecidas para mineração de textos em português: modelos e aplicações |
| Beneficiário: | Solange Oliveira Rezende |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 19/07665-4 - Centro de Inteligência Artificial |
| Beneficiário: | Fabio Gagliardi Cozman |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa em Engenharia |