The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
In applications such as social, energy, transportation, sensor, and neuronal networks, high-
dimensional data naturally reside on the vertices of weighted graphs. The emerging field of …
dimensional data naturally reside on the vertices of weighted graphs. The emerging field of …
More recent advances in (hyper) graph partitioning
In recent years, significant advances have been made in the design and evaluation of
balanced (hyper) graph partitioning algorithms. We survey trends of the past decade in …
balanced (hyper) graph partitioning algorithms. We survey trends of the past decade in …
Hierarchical graph transformer with adaptive node sampling
The Transformer architecture has achieved remarkable success in a number of domains
including natural language processing and computer vision. However, when it comes to …
including natural language processing and computer vision. However, when it comes to …
Convolutional neural networks on graphs with fast localized spectral filtering
In this work, we are interested in generalizing convolutional neural networks (CNNs) from
low-dimensional regular grids, where image, video and speech are represented, to high …
low-dimensional regular grids, where image, video and speech are represented, to high …
[图书][B] Recent advances in graph partitioning
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Graph reduction with spectral and cut guarantees
A Loukas - Journal of Machine Learning Research, 2019 - jmlr.org
Can one reduce the size of a graph without significantly altering its basic properties? The
graph reduction problem is hereby approached from the perspective of restricted spectral …
graph reduction problem is hereby approached from the perspective of restricted spectral …
Graph coarsening with neural networks
As large-scale graphs become increasingly more prevalent, it poses significant
computational challenges to process, extract and analyze large graph data. Graph …
computational challenges to process, extract and analyze large graph data. Graph …
Compact support biorthogonal wavelet filterbanks for arbitrary undirected graphs
This paper extends previous results on wavelet filterbanks for data defined on graphs from
the case of orthogonal transforms to more general and flexible biorthogonal transforms. As …
the case of orthogonal transforms to more general and flexible biorthogonal transforms. As …
Lean algebraic multigrid (LAMG): Fast graph Laplacian linear solver
OE Livne, A Brandt - SIAM Journal on Scientific Computing, 2012 - SIAM
Laplacian matrices of graphs arise in large-scale computational applications such as
semisupervised machine learning; spectral clustering of images, genetic data, and web …
semisupervised machine learning; spectral clustering of images, genetic data, and web …
A multiscale pyramid transform for graph signals
DI Shuman, MJ Faraji… - IEEE Transactions on …, 2015 - ieeexplore.ieee.org
Multiscale transforms designed to process analog and discrete-time signals and images
cannot be directly applied to analyze high-dimensional data residing on the vertices of a …
cannot be directly applied to analyze high-dimensional data residing on the vertices of a …