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Discrete Signal Processing on Graphs [PDF]
IEEE Transactions on Signal Processing, 2013 In social settings, individuals interact through webs of relationships. Each individual is a node in a complex network (or graph) of interdependencies and generates data, lots of data. We label the data by its source, or formally stated, we index the data by the nodes of the graph.
José M. F. Moura, Aliaksei Sandryhaila
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Graph Signal Processing: History, Development, Impact, and Outlook
IEEE Signal Processing Magazine, 2023 Signal processing (SP) excels at analyzing, processing, and inferring information defined over regular (first continuous, later discrete) domains such as time or space.
Antonio Ortega +2 more
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2023
Η παρούσα διατριβή πραγματεύτηκε μοντέλα επεξεργασίας σήματος που εφαρμόζονται σε γραφήματα. Η τετραγωνική κατά το ήμισυ ελαχιστοποίηση, οι εύρωστοι εκτιμητές, όπως ο διάμεσος και οι Μ-εκτιμητές, η κανονικοποίηση μέσω της l21 νόρμας, η ομαδική μέθοδος των ελαχίστων τετραγώνων αποτελούν τη βάση των προτεινόμενων προσεγγίσεων.
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Η παρούσα διατριβή πραγματεύτηκε μοντέλα επεξεργασίας σήματος που εφαρμόζονται σε γραφήματα. Η τετραγωνική κατά το ήμισυ ελαχιστοποίηση, οι εύρωστοι εκτιμητές, όπως ο διάμεσος και οι Μ-εκτιμητές, η κανονικοποίηση μέσω της l21 νόρμας, η ομαδική μέθοδος των ελαχίστων τετραγώνων αποτελούν τη βάση των προτεινόμενων προσεγγίσεων.
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GENERALIZED GRAPH SIGNAL PROCESSING
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018Graph signal processing (GSP) has become an important tool in many areas such as image processing, networking learning and analysis of social network data. In this paper, we propose a broader framework that not only encompasses traditional GSP as a special case, but also includes a hybrid framework of graph and classical signal processing over a ...
Feng Ji, Wee Peng Tay
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Graph-Projected Signal Processing
2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018In the past few years, Graph Signal Processing (GSP) has attracted a lot of interest for its aim at extending Fourier analysis to arbitrary discrete topologies described by graphs. Since it is essentially built upon analogies between classical temporal Fourier transforms and ring graphs spectrum, these extensions do not necessarily yield expected ...
Grelier, Nicolas +3 more
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Graph Error Effect in Graph Signal Processing
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018The first step in any graph signal processing (GSP) task is to learn the graph signal representation, i.e., to capture the dependence structure of the data into an adjacency matrix. Indeed, the adjacency matrix is typically not known a priori and has to be learned. However, it is learned with errors.
Vorobyov, Sergiy A. +3 more
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Graph Signal Processing: Overview, Challenges, and Applications [PDF]
Proceedings of the IEEE, 2018 To appear, Proceedings of the ...
Antonio Ortega +2 more
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Signal processing on graphs: Estimating the structure of a graph
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015This paper presents a computationally tractable algorithm for estimating the graph structure of graph signals is presented. The algorithm is demonstrated on simulated and real network time series datasets, and the performance of the new method is compared to that of related methods for estimating graph structure.
Jonathan Mei, José M. F. Moura
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Discrete Signal Processing on Graphs: Graph Filters
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013We propose a novel discrete signal processing framework for structured datasets that arise from social, economic, biological, and physical networks. Our framework extends traditional discrete signal processing theory to datasets with complex structure that can be represented by graphs, so that data elements are indexed by graph nodes and relations ...
José M. F. Moura, Aliaksei Sandryhaila
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Circulant structures and graph signal processing
2013 IEEE International Conference on Image Processing, 2013Linear shift-invariant processing of graph signals rests on circulant graphs and filters. The spatial features of circulant structures also permit shift-varying operations such as sampling. Their spectral features-as described by their Graph Fourier Transform profiles-enable novel multiscale signal processing systems and methods. To extend the reach of
Venkatesan N. Ekambaram +3 more
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