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02/09/2023
SF-SGL: Solver-Free Spectral Graph Learning from Linear Measurements
This work introduces a highly-scalable spectral graph densification fram...
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10/26/2022
HyperEF: Spectral Hypergraph Coarsening by Effective-Resistance Clustering
This paper introduces a scalable algorithmic framework (HyperEF) for spe...
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01/30/2022
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks
Graph neural networks (GNNs) have been increasingly deployed in various ...
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08/17/2021
HyperSF: Spectral Hypergraph Coarsening via Flow-based Local Clustering
Hypergraphs allow modeling problems with multi-way high-order relationsh...
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04/16/2021
SGL: Spectral Graph Learning from Measurements
This work introduces a highly scalable spectral graph densification fram...
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02/07/2021
SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation
A black-box spectral method is introduced for evaluating the adversarial...
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08/17/2020
SF-GRASS: Solver-Free Graph Spectral Sparsification
Recent spectral graph sparsification techniques have shown promising per...
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11/23/2019
GRASPEL: Graph Spectral Learning at Scale
Learning meaningful graphs from data plays important roles in many data ...
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11/04/2019
GRASS: Spectral Sparsification Leveraging Scalable Spectral Perturbation Analysis
Spectral graph sparsification aims to find ultra-sparse subgraphs whose ...
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10/06/2019
GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding
Graph embedding techniques have been increasingly deployed in a multitud...
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12/21/2018
Nearly-Linear Time Spectral Graph Reduction for Scalable Graph Partitioning and Data Visualization
This paper proposes a scalable algorithmic framework for spectral reduct...
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12/11/2018
Towards Scalable Spectral Sparsification of Directed Graphs
Recent spectral graph sparsification research allows constructing nearly...
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11/14/2017
Similarity-Aware Spectral Sparsification by Edge Filtering
In recent years, spectral graph sparsification techniques that can compu...
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10/12/2017
