Hierarchical Graph Pooling with Structure Learning
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Updated
Jul 12, 2021 - Python
Hierarchical Graph Pooling with Structure Learning
Experimental results obtained with the MinCutPool layer as presented in the 2020 ICML paper "Spectral Clustering with Graph Neural Networks for Graph Pooling"
AAAI 2020 - ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations
Official Code Repository for the paper "Accurate Learning of Graph Representations with Graph Multiset Pooling" (ICLR 2021)
Code for "Understanding Pooling in Graph Neural Networks" (TNNLS 2022).
Official Code Repository for the paper "Edge Representation Learning with Hypergraphs" (NeurIPS 2021)
Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport
Hierarchical Inter-Message Passing for Learning on Molecular Graphs
Pytorch and Tensorflow implementation of TVGNN, presented at ICML 2023.
Hierarchical Multi-View Graph Pooling with Structure Learning (TKDE-2021)
Self-Attention Graph Pooling [ICML-2019]
A repo for baseline of graph pooling.
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations [AAAI-2020]
Software implementation of Pyramidal Reservoir Graph Neural Networks
Code and dataset to test empirically the expressive power of graph pooling operators.
Using Graph Attention NN for image embedding and classification
Implementation of Graph pooling and clustering operation using Graph Neural Networks in PyTorch
HiCAP---Hierarchical Clustering-based Attention Pooling for Graph Representation Learning
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