Proceedings ArticleDOI
Tight framelets on graphs for multiscale data analysis
Yu Guang Wang,Xiaosheng Zhuang +1 more
- Vol. 11138, pp 100-111
TLDR
The construction and applications of decimated tight framelets on graphs based on graph clustering algorithms, where a coarse-grained chain of graphs can be constructed where a suitable orthonormal eigenpair can be deduced, are discussed.Abstract:
In this paper, we discuss the construction and applications of decimated tight framelets on graphs. Based on graph clustering algorithms, a coarse-grained chain of graphs can be constructed where a suitable orthonormal eigenpair can be deduced. Decimated tight framelets can then be constructed based on the orthonormal eigen-pair. Moreover, such tight framelets are associated with filter banks with which fast framelet transform algorithms can be realized. An explicit toy example of decimated tight framelets on a graph is provided.read more
Citations
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Journal ArticleDOI
Fast Haar Transforms for Graph Neural Networks
TL;DR: Haar convolution as discussed by the authors is a sparse and localized orthonormal system for a coarse-grained chain on the graph, which allows Fast Haar Transforms (FHTs) to be applied to graph convolutions.
Posted Content
Decimated Framelet System on Graphs and Fast G-Framelet Transforms.
TL;DR: This paper proposes a novel multiscale representation system for graph data, called decimated framelets, which form a localized tight frame on the graph which has linear computational complexity O(N) for a graph of size N.
Journal ArticleDOI
Cell graph neural networks enable the precise prediction of patient survival in gastric cancer
Yanan Wang,Yu Guang Wang,Changyuan Hu,Ming Li,Yanan Fan,Nina Otter,Ikuan Sam,H. Gou,Yiqun Hu,Terry Kwok,John Zalcberg,Alex Boussioutas,Roger J. Daly,Guido Montúfar,Pietro Liò,Dakang Xu,Geoffrey I. Webb,Jiangning Song +17 more
TL;DR: In this paper , a graph neural network-based approach was proposed for the digital staging of tumor microenvironment (TME) and precise prediction of patient survival in gastric cancer, which is formulated as either a binary (short-term and long-term ) or ternary (shortterm, medium-term , and longterm ) classification task.
Posted ContentDOI
Cell graph neural networks enable digital staging of tumour microenvironment and precisely predict patient survival in gastric cancer
Yanan Wang,Yu Guang Wang,Yu Guang Wang,Yu Guang Wang,Changyuan Hu,Ming Li,Yanan Fan,Nina Otter,Ikuan Sam,Hongquan Gou,Yiqun Hu,Terry Kwok,John Zalcberg,John Zalcberg,Alex Boussioutas,Roger J. Daly,Guido Montúfar,Guido Montúfar,Pietro Liò,Dakang Xu,Geoffrey I. Webb,Jiangning Song,Jiangning Song +22 more
TL;DR: Wang et al. as mentioned in this paper proposed a novel graph neural network-based approach, termed Cell-Graph Signature or CGSignature, powered by artificial intelligence, for digital staging of TME and precise prediction of patient survival in gastric cancer.
Journal ArticleDOI
Adaptive Directional Haar Tight Framelets on Bounded Domains for Digraph Signal Representations
Yuchen Xiao,Xiaosheng Zhuang +1 more
TL;DR: It is shown that the adaptive directional Haar tight framelet systems can be used for digraph signal representations.
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