One-class graph autoencoder: A new end-to-end, low-dimensional, and interpretable approach for node classification

Author(s)

Golo, Marcos Paulo Silva ; de Medeiros Jr, Jose Gilberto Barbosa de Medeiros ; Silva, Diego Furtado ; Marcacini, Ricardo Marcondes

• Document type: Journal article
• Source: INFORMATION SCIENCES; v. 708, p. 17-pg., 2025-03-06.

Abstract

One-class learning (OCL) for graph neural networks (GNNs) comprises a set of techniques applied when real-world problems are modeled through graphs and have a single class of interest. These methods may employ a two-step strategy: first representing the graph and then classifying its nodes. End-to-end methods learn the node representations while classifying the nodes in OCL process. We highlight three main gaps in this literature: (i) non-customized representations for OCL; (ii) the lack of constraints on hypersphere learning; and (iii) the lack of interpretability. This paper presents One-cLass Graph Autoencoder (OLGA), a new OCL for GNN approach. OLGA is an end-to-end method that learns low-dimensional representations for nodes while encapsulating interest nodes through a proposed and new hypersphere loss function. Furthermore, OLGA combines this new hypersphere loss with the graph autoencoder reconstruction loss to improve model learning. The reconstruction loss is a constraint to the sole use of the hypersphere loss that can bias the model to encapsulate all nodes. Finally, our low-dimensional representation makes the OLGA interpretable since we can visualize the representation learning at each epoch. OLGA achieved state-of-the-art results and outperformed six other methods with statistical significance while maintaining the learning process interpretability with its low-dimensional representations. (AU)

• 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: 23/10100-4 - Development of Large Language Models for Applications in the Legal Domain
• Grantee: Ricardo Marcondes Marcacini
• Support Opportunities: Research Grants - Research in Public Policies
• FAPESP's process: 23/02680-0 - Transfer of Learning to Deal with Devices Heterogeneity
• Grantee: José Gilberto Barbosa de Medeiros Júnior
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 19/07665-4 - Center for Artificial Intelligence
• Grantee: Fabio Gagliardi Cozman
• Support Opportunities: Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program