Hierarchical message-passing graph neural networks

• Published in: Data mining and knowledge discovery Vol. 37; no. 1; pp. 381 - 408
• Main Authors: Zhong, Zhiqiang, Li, Cheng-Te, Pang, Jun
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.01.2023; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Chemistry and Earth Sciences; Coders; Computer Science; Data Mining and Knowledge Discovery; Empirical analysis; Graph neural networks; Graph theory; Graphs; Information Storage and Retrieval; Machine learning; Message passing; Network analysis; Neural networks; Nodes; Physics; Semantics; Special Issue of the Journal Track of ECML PKDD 2022; Statistics for Engineering; Representation learning; Hierarchical message-passing; Graph neural networks; Long range communication; Hierarchical structure

Graph Neural Networks (GNNs) have become a prominent approach to machine learning with graphs and have been increasingly applied in a multitude of domains. Nevertheless, since most existing GNN models are based on flat message-passing mechanisms, two limitations need to be tackled: (i) they are cost...