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  3. Dynamic k-graphs: an algorithm for dynamic graph learning and temporal graph signal clustering

Dynamic k-graphs: an algorithm for dynamic graph learning and temporal graph signal clustering

Araghi, H ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.23919/Eusipco47968.2020.9287661
  3. Publisher: European Signal Processing Conference, EUSIPCO , 2021
  4. Abstract:
  5. Graph signal processing (GSP) have found many applications in different domains. The underlying graph may not be available in all applications, and it should be learned from the data. There exist complicated data, where the graph changes over time. Hence, it is necessary to estimate the dynamic graph. In this paper, a new dynamic graph learning algorithm, called dynamic K-graphs, is proposed. This algorithm is capable of both estimating the time-varying graph and clustering the temporal graph signals. Numerical experiments demonstrate the high performance of this algorithm compared with other algorithms. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved
  6. Keywords:
  7. Clustering algorithms ; Learning algorithms ; Signal processing ; Different domains ; Dynamic graph ; Numerical experiments ; Temporal graphs ; Time-varying graphs ; Underlying graphs ; Graph algorithms
  8. Source: 28th European Signal Processing Conference, EUSIPCO 2020, 24 August 2020 through 28 August 2020 ; Volume 2021-January , 2021 , Pages 2195-2199 ; 22195491 (ISSN); 9789082797053 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/9287661