Some graphs have rich hierarchy structure. For this type of graph, we can use hierarchical clustering methods.

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When a graph-level representation is needed to tackle graph-level tasks, node-level representations can be aggregated into a single graph-level representation. This aggregation process is referred to as a readout operation.

Graph Readout Operation

Zhang, Z., Cui, P., & Zhu, W. (2020). Deep learning on graphs: A survey. IEEE Transactions on Knowledge and Data Engineering.

Deep Learning on Graphs: A Survey

This is a basic type of readout operation, it uses simple statistics such as summation, averaging, max-pooling to aggregate nodes representation to graph representation. 

Statistics Type of Readout Operation

Hierarchical Clustering Type of Readout Operation

This method imposes a node order on the graph, and then use 1-D pooling as Convolutional Neural Network to aggregate node representation.

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