Modelling Graph Neural Network by Aggregating the Activation Maps of Self-Organizing Map

Autor(es)

Martins, Luan V. de C. ; Ji, Donghong ; Liang, Zhao

• Tipo de documento: Artigo Científico
• Fonte: 2024 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN 2024; v. N/A, p. 6-pg., 2024-01-01.

Resumo

The field of Graph Neural Networks (GNNs) has garnered significant attention for leveraging Neural Networks to analyze graph data. However, the inherent limitations of GNNs, arising from their aggregation function and injectivity constraints, restrict their ability to distinguish various topological neighborhood structures accurately. Consequently, after aggregation, different underlying graph structures may not be distinctly identified, impeding the model's representation capabilities. In this study, we introduce a novel GNN model that incorporates Self-Organizing Maps (SOM) to enhance the representation of node attributes and characterize the graph's neighborhood structure. Leveraging SOM, we transform the node's attribute space into a fixed-size grid, enabling the representation of neighborhood topology by overlaying activated regions, which allows the model to learn association rules based on the presence or absence of neighboring attributes. This process generates an injective graph embedding that uniquely captures different neighborhood structures. We evaluate the model on real-world datasets to assess its' performance, analyzing how SOM parameters influence its behavior and effectiveness. Through extensive experimentation, we demonstrate that our model achieves competitive and robust results with well-established GNN models. Our approach showcases the potential of using SOM to overcome limitations in current GNN architectures, providing a more effective and nuanced representation of graph data. (AU)

• Processo FAPESP: 20/15129-2 - Classificação de sinal EEG entre focal e não-focal por meio de aprendizado profundo
• Beneficiário: Luan Vinícius de Carvalho Martins
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• 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