TransGNN: A Transductive Graph Neural Network with Graph Dynamic Embedding

Author(s)

Anghinoni, Leandro ; Zhu, Yu-Tao ; Ji, Donghong ; Zhao, Liang ; IEEE

• Document type: Journal article
• Source: 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN; v. N/A, p. 8-pg., 2023-01-01.

Abstract

Graph Neural Networks (GNNs) have become a rapidly growing field, due to their ability to capture the relationship among data, instead of only learning from the attribute of the data. The core of any GNN is the graph embedding generation by message passing mechanisms. In this work we propose a new message passing technique based on the Particle Competition and Cooperation (PCC) model, originally developed for community detection in graphs. The proposed framework performs a transductive learning in the network and passes the learned information to the nodes, prior to the inductive learning performed by traditional GNN schemes. The new GNN presents attractive features which overcomes the over-smoothing problem of traditional GNNs and shows promising results in terms of classification accuracy, computational cost and learning with very small quantity of labeled data. (AU)

• 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