Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling

24 Oct 2019 β€’ Filippo Maria Bianchi β€’ Daniele Grattarola β€’ Lorenzo Livi β€’ Cesare Alippi

In graph neural networks (GNNs), pooling operators compute local summaries of input graphs to capture their global properties, and they are fundamental for building deep GNNs that learn hierarchical representations. In this work, we propose the Node Decimation Pooling (NDP), a pooling operator for GNNs that generates coarser graphs while preserving the overall graph topology... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Graph Classification 5pt. Bench-Easy NDP Accuracy 97.9 # 1
Graph Classification Bench-hard NDP Accuracy 72.6 # 1
Graph Classification COLLAB NDP Accuracy 79.1% # 14
Graph Classification D&D NDP Accuracy 72% # 38
Graph Classification ENZYMES NDP Accuracy 43.9% # 27
Graph Classification MUTAG NDP Accuracy 84.7% # 43
Graph Classification Mutagenicity NDP Accuracy 78.1 # 2
Graph Classification NCI1 NDP Accuracy 73.5% # 34
Graph Classification PROTEINS Graph2Vec Accuracy 73.3% # 57
Graph Classification REDDIT-B NDP Accuracy 84.3 # 8

Methods used in the Paper


METHOD TYPE
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