• Corpus ID: 229153615

Decimated Framelet System on Graphs and Fast G-Framelet Transforms

@article{Zheng2020DecimatedFS,
  title={Decimated Framelet System on Graphs and Fast G-Framelet Transforms},
  author={Xuebin Zheng and Bingxin Zhou and Yu Guang Wang and Xiaosheng Zhuang},
  journal={J. Mach. Learn. Res.},
  year={2020},
  volume={23},
  pages={18:1-18:68}
}
Graph representation learning has many real-world applications, from super-resolution imaging, 3D computer vision to drug repurposing, protein classification, social networks analysis. An adequate representation of graph data is vital to the learning performance of a statistical or machine learning model for graph-structured data. In this paper, we propose a novel multiscale representation system for graph data, called decimated framelets, which form a localized tight frame on the graph. The… 
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11 Citations

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This paper derives an MRA tight wavelet frame with three generators from the pseudosplines of type II, and analyzes that three wavelet functions have the highest possible order of vanishing moments, which matches the order of the approximationOrder of the framelet system provided by the refinable function.

A Survey on Graph Representation Learning Methods

An overview of non-GNN graph embedding methods, which are based on techniques such as random walks, temporal point processes and neural network learning methods, and GNN-based methods which are the application of deep learning on graph data are provided.

Graph Neural Network for Local Corruption Recovery

This work tackles the graph recovery problem from local poisons by a robustness representation learning by identifying regional graph perturbations and formulates a robust hidden feature representation for GNNs.

Robust Graph Representation Learning for Local Corruption Recovery

This work establishes a graph learning scheme that automati-cally detects (locally) corrupted feature attributes and recovers robust embedding for prediction tasks and can recover a robust graph representation from black-box poisoning and achieve excellent performance.

Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks

Message Passing Simplicial Networks (MPSNs), a class of models that perform message passing on simplicial complexes (SCs) are proposed, and a Simplicial Weisfeiler-Lehman (SWL) colouring procedure is introduced for distinguishing non-isomorphic SCs.

Cell graph neural networks enable the precise prediction of patient survival in gastric cancer

The study suggests the feasibility and benefits of such an artificial intelligence-powered digital staging system in diagnostic pathology and precision oncology and improves the assessment of the link between the TME spatial patterns and patient prognosis.

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Compared to ReLU, shrinkage activation improves model performance on denoising and signal compression: noises in both node and structure can be significantly reduced by accurately cutting off the high-pass coefficients from framelet decomposition, and the signal can be compressed to less than half its original size with well-preserved prediction performance.

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