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- Title
Real Quadratic-Form-Based Graph Pooling for Graph Neural Networks.
- Authors
Liu, Youfa; Chen, Guo
- Abstract
Graph neural networks (GNNs) have developed rapidly in recent years because they can work over non-Euclidean data and possess promising prediction power in many real-word applications. The graph classification problem is one of the central problems in graph neural networks, and aims to predict the label of a graph with the help of training graph neural networks over graph-structural datasets. The graph pooling scheme is an important part of graph neural networks for the graph classification objective. Previous works typically focus on using the graph pooling scheme in a linear manner. In this paper, we propose the real quadratic-form-based graph pooling framework for graph neural networks in graph classification. The quadratic form can capture a pairwise relationship, which brings a stronger expressive power than existing linear forms. Experiments on benchmarks verify the effectiveness of the proposed graph pooling scheme based on the quadratic form in graph classification tasks.
- Subjects
ARTIFICIAL intelligence; DEEP learning; ARTIFICIAL neural networks; RECOMMENDER systems; MULTILAYER perceptrons
- Publication
Machine Learning & Knowledge Extraction, 2022, Vol 4, Issue 3, p580
- ISSN
2504-4990
- Publication type
Article
- DOI
10.3390/make4030027