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Sampling and Recovery of Graph Signals [PDF]
, 2018 The aim of this chapter is to give an overview of the recent advances related to sampling and recovery of signals defined over graphs. First, we illustrate the conditions for perfect recovery of bandlimited graph signals from samples collected over a selected set of vertexes. Then, we describe some sampling design criteria proposed in the literature to
Paolo Di Lorenzo +2 more
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Graph products of groups [PDF]
, 1990 In the 1970's Baudisch introduced the idea of the semifree group, that is, a group in which the only relators are commutators of generators. Baudisch was mainly concerned with subgroup problems, employing length arguments on the elements of these groups.
Green, E.R, Green, Elisabeth Ruth
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Sampling large data on graphs [PDF]
2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2014 We consider the problem of sampling from data defined on the nodes of a weighted graph, where the edge weights capture the data correlation structure. As shown recently, using spectral graph theory one can define a cut-off frequency for the bandlimited graph signals that can be reconstructed from a given set of samples (i.e., graph nodes). In this work,
Ilan Shomorony, Amir Salman Avestimehr
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Large Graph Sampling Algorithm for Frequent Subgraph Mining
IEEE Access, 2021 Large graph networks frequently appear in the latest applications. Their graph structures are very large, and the interaction among the vertices makes it difficult to split the structures into separate multiple structures, thus increasing the difficulty ...
Tianyu Zheng, Li Wang
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Near-Optimal Graph Signal Sampling by Pareto Optimization
Sensors, 2021 In this paper, we focus on the bandlimited graph signal sampling problem. To sample graph signals, we need to find small-sized subset of nodes with the minimal optimal reconstruction error.
Dongqi Luo +4 more
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BC tree-based spectral sampling for big complex network visualization
Applied Network Science, 2021 Graph sampling methods have been used to reduce the size and complexity of big complex networks for graph mining and visualization. However, existing graph sampling methods often fail to preserve the connectivity and important structures of the original ...
Jingming Hu +7 more
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Continuous Latent Spaces Sampling for Graph Autoencoder
Applied Sciences, 2023 This paper proposes colaGAE, a self-supervised learning framework for graph-structured data. While graph autoencoders (GAEs) commonly use graph reconstruction as a pretext task, this simple approach often yields poor model performance.
Zhongyu Li +4 more
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Graph sampling with determinantal processes [PDF]
2017 25th European Signal Processing Conference (EUSIPCO), 2017 5 pages, 1 ...
Nicolas Tremblay +2 more
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European Journal of Remote Sensing, 2020 In recent years, deep learning has drawn increasing attention in the field of hyperspectral remote sensing image classification and has achieved great success.
Xibing Zuo +5 more
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Kinematic Graph for Motion Planning of Robotic Manipulators
Robotics, 2022 We introduce a kinematic graph in this article. A kinematic graph results from structuring the data obtained from the sampling method for sampling-based motion planning algorithms in robotics with the motivation to adapt the method to the positioning ...
Burkhard Corves, Amir Shahidi
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