Haar Graph Pooling
Yu Guang Wang, Ming Li, Zheng Ma, Guido Montufar, Xiaosheng Zhuang,, Yanan Fan

TL;DR
HaarGraph Pooling introduces a novel graph pooling method based on Haar transforms, enabling GNNs to efficiently compress graph features while preserving structural information, leading to improved performance on classification and regression tasks.
Contribution
The paper proposes HaarPooling, a new graph pooling technique using Haar transforms that effectively compresses graph data while maintaining structural details, enhancing GNN capabilities.
Findings
Achieves state-of-the-art results on graph classification tasks.
Effectively compresses graph features into fixed-size vectors.
Preserves structural information through Haar wavelet domain filtering.
Abstract
Deep Graph Neural Networks (GNNs) are useful models for graph classification and graph-based regression tasks. In these tasks, graph pooling is a critical ingredient by which GNNs adapt to input graphs of varying size and structure. We propose a new graph pooling operation based on compressive Haar transforms -- HaarPooling. HaarPooling implements a cascade of pooling operations; it is computed by following a sequence of clusterings of the input graph. A HaarPooling layer transforms a given input graph to an output graph with a smaller node number and the same feature dimension; the compressive Haar transform filters out fine detail information in the Haar wavelet domain. In this way, all the HaarPooling layers together synthesize the features of any given input graph into a feature vector of uniform size. Such transforms provide a sparse characterization of the data and preserve the…
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Code & Models
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Taxonomy
TopicsDiabetes Management and Research
MethodsConvolution
