Computation of Graph Fourier Transform Centrality Using Graph Filter
In this paper, the computation of graph Fourier transform centrality (GFTC) of complex network using graph filter is presented. For conventional computation method, it needs to use the non-sparse transform matrix of graph Fourier transform (GFT) to compute GFTC scores. To reduce the computational co...
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| Language: | English |
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2024-01-01
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| Series: | IEEE Open Journal of Circuits and Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10500497/ |
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| author | Chien-Cheng Tseng Su-Ling Lee |
| author_facet | Chien-Cheng Tseng Su-Ling Lee |
| author_sort | Chien-Cheng Tseng |
| collection | DOAJ |
| description | In this paper, the computation of graph Fourier transform centrality (GFTC) of complex network using graph filter is presented. For conventional computation method, it needs to use the non-sparse transform matrix of graph Fourier transform (GFT) to compute GFTC scores. To reduce the computational complexity of GFTC, a linear algebra method based on Frobenius norm of error matrix is applied to convert the spectral-domain GFTC computation task to vertex-domain one such that GFTC can be computed by using polynomial graph filtering method. There are two kinds of designs of graph filters to be studied. One is the graph-aware method; the other is the graph-unaware method. The computational complexity comparison and experimental results show that the proposed graph filter method is more computationally efficient than conventional GFT method because the sparsity of Laplacian matrix is used in the implementation structure. Finally, the centrality computations of social network, metro network and sensor network are used to demonstrate the effectiveness of the proposed GFTC computation method using graph filter. |
| format | Article |
| id | doaj-art-085f5bdfe41c408baa3a5344d0c52d4b |
| institution | Kabale University |
| issn | 2644-1225 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Circuits and Systems |
| spelling | doaj-art-085f5bdfe41c408baa3a5344d0c52d4b2025-01-21T00:02:45ZengIEEEIEEE Open Journal of Circuits and Systems2644-12252024-01-015698010.1109/OJCAS.2023.331794410500497Computation of Graph Fourier Transform Centrality Using Graph FilterChien-Cheng Tseng0https://orcid.org/0000-0002-4235-8567Su-Ling Lee1https://orcid.org/0000-0003-1043-7866Department of Computer and Communication Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, TaiwanDepartment of Computer Science and Information Engineering, Chang Jung Christian University, Tainan, TaiwanIn this paper, the computation of graph Fourier transform centrality (GFTC) of complex network using graph filter is presented. For conventional computation method, it needs to use the non-sparse transform matrix of graph Fourier transform (GFT) to compute GFTC scores. To reduce the computational complexity of GFTC, a linear algebra method based on Frobenius norm of error matrix is applied to convert the spectral-domain GFTC computation task to vertex-domain one such that GFTC can be computed by using polynomial graph filtering method. There are two kinds of designs of graph filters to be studied. One is the graph-aware method; the other is the graph-unaware method. The computational complexity comparison and experimental results show that the proposed graph filter method is more computationally efficient than conventional GFT method because the sparsity of Laplacian matrix is used in the implementation structure. Finally, the centrality computations of social network, metro network and sensor network are used to demonstrate the effectiveness of the proposed GFTC computation method using graph filter.https://ieeexplore.ieee.org/document/10500497/Graph signal processingcomplex networknode centralitygraph Fourier transformgraph filter |
| spellingShingle | Chien-Cheng Tseng Su-Ling Lee Computation of Graph Fourier Transform Centrality Using Graph Filter IEEE Open Journal of Circuits and Systems Graph signal processing complex network node centrality graph Fourier transform graph filter |
| title | Computation of Graph Fourier Transform Centrality Using Graph Filter |
| title_full | Computation of Graph Fourier Transform Centrality Using Graph Filter |
| title_fullStr | Computation of Graph Fourier Transform Centrality Using Graph Filter |
| title_full_unstemmed | Computation of Graph Fourier Transform Centrality Using Graph Filter |
| title_short | Computation of Graph Fourier Transform Centrality Using Graph Filter |
| title_sort | computation of graph fourier transform centrality using graph filter |
| topic | Graph signal processing complex network node centrality graph Fourier transform graph filter |
| url | https://ieeexplore.ieee.org/document/10500497/ |
| work_keys_str_mv | AT chienchengtseng computationofgraphfouriertransformcentralityusinggraphfilter AT sulinglee computationofgraphfouriertransformcentralityusinggraphfilter |