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Topology-Aware Graph Pooling Networks

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Summary: Graph neural networks have achieved great success in learning node representations for various graph tasks. Graph representation learning requires graph pooling, which is challenging due to variable sizes and isomorphic structures of graphs. In this work, we propose second-order pooling as a graph pooling method, which naturally addresses these challenges and allows the utilization of information from all nodes. We also propose novel global graph pooling methods based on second-order pooling and demonstrate their effectiveness and superiority through thorough experiments.

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