Abstract
Recently, graph neural networks emerged as the leading machine learning architecture for supervised learning with graph and relational input. This chapter gives an overview of GNNs for graph classification, i.e., GNNs that learn a graphlevel output. Since GNNs compute node-level representations, pooling layers, i.e., layers that learn graph-level representations from node-level representations, are crucial components for successful graph classification. Hence, we give a thorough overview of pooling layers. Further, we overview recent research in understanding GNN’s limitations for graph classification and progress in overcoming them. Finally, we survey some graph classification applications of GNNs and overview benchmark datasets for empirical evaluation.
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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Morris, C. (2022). Graph Neural Networks: Graph Classification. In: Wu, L., Cui, P., Pei, J., Zhao, L. (eds) Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-16-6054-2_9
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DOI: https://doi.org/10.1007/978-981-16-6054-2_9
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Publisher Name: Springer, Singapore
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Online ISBN: 978-981-16-6054-2
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