Finding and Evaluating Community Structures in Spatial Networks
Community detection can reveal unknown spatial structures embedded in spatial networks. Current spatial community detection methods are mostly modularity-based. However, due to the lack of appropriate spatial networks serving as a benchmark, the accuracy and effectiveness of these methods have not b...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/12/5/187 |
| _version_ | 1827741122267447296 |
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| author | You Wan Xicheng Tan Hua Shu |
| author_facet | You Wan Xicheng Tan Hua Shu |
| author_sort | You Wan |
| collection | DOAJ |
| description | Community detection can reveal unknown spatial structures embedded in spatial networks. Current spatial community detection methods are mostly modularity-based. However, due to the lack of appropriate spatial networks serving as a benchmark, the accuracy and effectiveness of these methods have not been tested sufficiently so far. This study first introduced a spatial autoregressive and gravity model united method (SARGM) to simulate benchmark spatial networks with known regional distributions. Then, a novel spectral clustering-based spatial community detection method (SCSCD) was proposed to identify spatial communities from eight kinds of benchmark spatial networks. Comparative experiments on SCSCD and three other methods showed that SCSCD performed the best in accuracy and effectiveness. Moreover, the scale parameter and the community number setting of the SCSCD were investigated experimentally. Finally, a case study was applied to the SCSCD to demonstrate its ability to extract the internal community structure of a high-speed train network in China. |
| first_indexed | 2024-03-11T03:40:12Z |
| format | Article |
| id | doaj.art-10b334e9e7ea463cbfca169f21fb899a |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-11T03:40:12Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-10b334e9e7ea463cbfca169f21fb899a2023-11-18T01:35:54ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-05-0112518710.3390/ijgi12050187Finding and Evaluating Community Structures in Spatial NetworksYou Wan0Xicheng Tan1Hua Shu2School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaSchool of Computer Science and Information Engineering, Hubei University, Wuhan 430062, ChinaCommunity detection can reveal unknown spatial structures embedded in spatial networks. Current spatial community detection methods are mostly modularity-based. However, due to the lack of appropriate spatial networks serving as a benchmark, the accuracy and effectiveness of these methods have not been tested sufficiently so far. This study first introduced a spatial autoregressive and gravity model united method (SARGM) to simulate benchmark spatial networks with known regional distributions. Then, a novel spectral clustering-based spatial community detection method (SCSCD) was proposed to identify spatial communities from eight kinds of benchmark spatial networks. Comparative experiments on SCSCD and three other methods showed that SCSCD performed the best in accuracy and effectiveness. Moreover, the scale parameter and the community number setting of the SCSCD were investigated experimentally. Finally, a case study was applied to the SCSCD to demonstrate its ability to extract the internal community structure of a high-speed train network in China.https://www.mdpi.com/2220-9964/12/5/187spectral clusteringspatial community detectionspatial community evaluationbenchmark spatial network |
| spellingShingle | You Wan Xicheng Tan Hua Shu Finding and Evaluating Community Structures in Spatial Networks ISPRS International Journal of Geo-Information spectral clustering spatial community detection spatial community evaluation benchmark spatial network |
| title | Finding and Evaluating Community Structures in Spatial Networks |
| title_full | Finding and Evaluating Community Structures in Spatial Networks |
| title_fullStr | Finding and Evaluating Community Structures in Spatial Networks |
| title_full_unstemmed | Finding and Evaluating Community Structures in Spatial Networks |
| title_short | Finding and Evaluating Community Structures in Spatial Networks |
| title_sort | finding and evaluating community structures in spatial networks |
| topic | spectral clustering spatial community detection spatial community evaluation benchmark spatial network |
| url | https://www.mdpi.com/2220-9964/12/5/187 |
| work_keys_str_mv | AT youwan findingandevaluatingcommunitystructuresinspatialnetworks AT xichengtan findingandevaluatingcommunitystructuresinspatialnetworks AT huashu findingandevaluatingcommunitystructuresinspatialnetworks |