{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:24:16Z","timestamp":1772252656667,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T00:00:00Z","timestamp":1647907200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2020B121202019"],"award-info":[{"award-number":["2020B121202019"]}]},{"name":"Sichuan Science and Technology Program","award":["2020JDTD0003"],"award-info":[{"award-number":["2020JDTD0003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Landslide monitoring plays an important role in predicting, forecasting and preventing landslides. Quantitative explorations at the subject level and fine-scale knowledge in landslide monitoring research can be used to provide information and references for landslide monitoring status analysis and disaster management. In the context of the large amount of network information, it is difficult to clearly determine and display the domain topic hierarchy and knowledge structure. This paper proposes a landslide monitoring knowledge discovery method that combines K-core decomposition and Louvain algorithms. In this method, author keywords are used as nodes to construct a weighted co-occurrence network, and a pruning standard value is defined as K. The K-core approach is used to decompose the network into subgraphs. Combined with the unsupervised Louvain algorithm, subgraphs are divided into different topic communities by setting a modularity change threshold, which is used to establish a topic hierarchy and identify fine-scale knowledge related to landslide monitoring. Based on the Web of Science, a comparative experiment involving the above method and a high-frequency keyword subgraph method for landslide monitoring knowledge discovery is performed. The results show that the run time of the proposed method is significantly less than that of the traditional method.<\/jats:p>","DOI":"10.3390\/ijgi11040217","type":"journal-article","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T14:55:35Z","timestamp":1647960935000},"page":"217","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Knowledge Discovery Method for Landslide Monitoring Based on K-Core Decomposition and the Louvain Algorithm"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6394-8374","authenticated-orcid":false,"given":"Ping","family":"Wang","sequence":"first","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Xingdong","family":"Deng","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning and Design Survey Research Institute, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning and Design Survey Research Institute, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}]},{"given":"Liang","family":"Guo","sequence":"additional","affiliation":[{"name":"Guangzhou Urban Planning and Design Survey Research Institute, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}]},{"given":"Jun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Lin","family":"Fu","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Yakun","family":"Xie","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Weilian","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Jianbo","family":"Lai","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105525","DOI":"10.1016\/j.enggeo.2020.105525","article-title":"Landslide monitoring using seismic refraction tomography\u2014The importance of incorporating topographic variations","volume":"268","author":"Whiteley","year":"2020","journal-title":"Eng. 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