What is recorded here?
Learns soft node-to-cluster assignments and coarsens features and adjacency through S-transpose Z and S-transpose A S, enabling hierarchical graph neural networks.
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Scientific scope
Six original authors. Learned hierarchical pooling is not exact graph isomorphism, ground-truth community recovery or universally sparse coarsened graphs; assignment and dense coarsening introduce costs. The generic graph-learning evaluations do not establish arbitrary downstream application benefits.
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People and systems
Rex Ying; Jiaxuan You; Christopher Morris; Xiang Ren; William L. Hamilton; Jure Leskovec
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English catalogue narrative
DiffPool differentiable hierarchical graph pooling
Learns soft node-to-cluster assignments and coarsens features and adjacency through S-transpose Z and S-transpose A S, enabling hierarchical graph neural networks.
Scientific scope
Six original authors. Learned hierarchical pooling is not exact graph isomorphism, ground-truth community recovery or universally sparse coarsened graphs; assignment and dense coarsening introduce costs. The generic graph-learning evaluations do not establish arbitrary downstream application benefits.
People and systems
Rex Ying; Jiaxuan You; Christopher Morris; Xiang Ren; William L. Hamilton; Jure Leskovec