标题: Finding and Evaluating Community Structures in Spatial Networks

作者: Wan, Y (Wan, You); Tan, XC (Tan, Xicheng); Shu, H (Shu, Hua)

来源出版物: ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 卷: 12 期: 5 文献号: 187 DOI: 10.3390/ijgi12050187 出版年: MAY 4 2023

摘要: 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.

作者关键词: spectral clustering; spatial community detection; spatial community evaluation; benchmark spatial network

地址: [Wan, You] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Wan, You] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Peoples R China.

[Tan, Xicheng] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China.

[Shu, Hua] Hubei Univ, Sch Comp Sci & Informat Engn, Wuhan 430062, Peoples R China.

通讯作者地址: Shu, H (通讯作者)，Hubei Univ, Sch Comp Sci & Informat Engn, Wuhan 430062, Peoples R China.

电子邮件地址: wanyou@whu.edu.cn; shuh@hubu.edu.cn

影响因子：3.4