MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning

Understand

How to utilize deep learning methods for graph classification tasks has attracted considerable research attention in the past few years.

• Regarding graph classification tasks, the graphs to be classified may have various graph sizes (i.e., different number of nodes and edges) and have various graph properties (e.g., average node degree, diameter, and clustering coefficient).
• The diverse property of graphs has imposed significant challenges on existing graph learning techniques since diverse graphs have different best-fit hyperparameters.
• It is difficult to learn graph features from a set of diverse graphs by a unified graph neural network.