This work introduces a highly-scalable spectral graph densification fram...
This paper introduces a scalable algorithmic framework (HyperEF) for spe...
Graph neural networks (GNNs) have been increasingly deployed in various
...
Hypergraphs allow modeling problems with multi-way high-order relationsh...
This work introduces a highly scalable spectral graph densification fram...
A black-box spectral method is introduced for evaluating the adversarial...
Recent spectral graph sparsification techniques have shown promising
per...
Learning meaningful graphs from data plays important roles in many data
...
Spectral graph sparsification aims to find ultra-sparse subgraphs whose
...
Graph embedding techniques have been increasingly deployed in a multitud...
This paper proposes a scalable algorithmic framework for spectral reduct...
Recent spectral graph sparsification research allows constructing
nearly...
In recent years, spectral graph sparsification techniques that can compu...
The eigendeomposition of nearest-neighbor (NN) graph Laplacian matrices ...