{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:36:20Z","timestamp":1760402180145,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T00:00:00Z","timestamp":1588291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research &amp; Development (R&amp;D) Plan","award":["2018YFC0806900"],"award-info":[{"award-number":["2018YFC0806900"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71673292","71673294"],"award-info":[{"award-number":["71673292","71673294"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Social Science Foundation of China","award":["17CGL047"],"award-info":[{"award-number":["17CGL047"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Recently, spatial interaction analysis of online social networks has become a big concern. Early studies of geographical characteristics analysis and community detection in online social networks have shown that nodes within the same community might gather together geographically. However, the method of community detection is based on the idea that there are more links within the community than that connect nodes in different communities, and there is no analysis to explain the phenomenon. The statistical models for network analysis usually investigate the characteristics of a network based on the probability theory. This paper analyzes a series of statistical models and selects the MDND model to classify links and nodes in social networks. The model can achieve the same performance as the community detection algorithm when analyzing the structure in the online social network. The construction assumption of the model explains the reasons for the geographically aggregating of nodes in the same community to a degree. The research provides new ideas and methods for nodes classification and geographic characteristics analysis of online social networks and mobile communication networks and makes up for the shortcomings of community detection methods that do not explain the principle of network generation. A natural progression of this work is to geographically analyze the characteristics of social networks and provide assistance for advertising delivery and Internet management.<\/jats:p>","DOI":"10.3390\/ijgi9050290","type":"journal-article","created":{"date-parts":[[2020,5,4]],"date-time":"2020-05-04T03:29:39Z","timestamp":1588562979000},"page":"290","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Geographical Structural Features of the WeChat Social Networks"],"prefix":"10.3390","volume":"9","author":[{"given":"Chuan","family":"Ai","sequence":"first","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hailiang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9426-5303","authenticated-orcid":false,"given":"Weihui","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Management, Fudan University, Shanghai 200433, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaogang","family":"Qiu","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., and Bhattacharjee, B. (2007, January 24\u201326). Measurement and Analysis of Online Social Networks. Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, IMC \u201907, San Diego, CA, USA.","DOI":"10.1145\/1298306.1298311"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1007\/s10955-014-1024-9","article-title":"Saving Human Lives: What Complexity Science and Information Systems can Contribute","volume":"158","author":"Helbing","year":"2015","journal-title":"J. Stat. Phys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"L07003","DOI":"10.1088\/1742-5468\/2009\/07\/L07003","article-title":"Urban gravity: A model for inter-city telecommunication flows","volume":"2009","author":"Krings","year":"2009","journal-title":"J. Stat. Mech. Theory Exp."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Roth, C., Kang, S.M., Batty, M., and Barth\u00e9lemy, M. (2011). Structure of Urban Movements: Polycentric Activity and Entangled Hierarchical Flows. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0015923"},{"key":"ref_5","unstructured":"Vincent, G.K., and Thomas, I. (2010). Regions and borders of mobile telephony in Belgium and in the Brussels metropolitan zone. Bruss. Stud., 2."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ratti, C., Sobolevsky, S., Calabrese, F., Andris, C., Reades, J., Martino, M., Claxton, R., and Strogatz, S.H. (2010). Redrawing the Map of Great Britain from a Network of Human Interactions. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0014248"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Moyano, L.G., Thomae, O.R.M., and Frias-Martinez, E. (2012, January 10). Uncovering the Spatio-temporal Structure of Social Networks Using Cell Phone Records. Proceedings of the IEEE International Conference on Data Mining Workshops, Brussels, Belgium.","DOI":"10.1109\/ICDMW.2012.132"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"270","DOI":"10.2307\/143711","article-title":"Geography as Spatial Interaction","volume":"57","author":"Siddall","year":"1981","journal-title":"Econ. Geogr."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, B., Wang, Y., Wang, R., Zhu, Z., Ma, L., Qiu, X., and Dai, W. (2020). The Gray-Box Based Modeling Approach Integrating Both Mechanism-Model and Data-Model: The Case of Atmospheric Contaminant Dispersion. Symmetry, 2.","DOI":"10.3390\/sym12020254"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1111\/j.1467-8306.1992.tb01899.x","article-title":"Modeling Interregional Interaction: Implications for Defining Functional Regions","volume":"82","author":"Noronha","year":"1992","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_11","first-page":"132","article-title":"The Information Age: Economy, Society and Culture","volume":"1","author":"Kumar","year":"2009","journal-title":"Rise Netw. Soc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1088\/1367-2630\/9\/6\/179","article-title":"Analysis of a large-scale weighted network of one-to-one human communication","volume":"9","author":"Onnela","year":"2007","journal-title":"New J. Phys."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1038\/nature05670","article-title":"Quantifying social group evolution","volume":"446","author":"Palla","year":"2007","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5317","DOI":"10.1016\/j.physa.2008.05.014","article-title":"Geographical dispersal of mobile communication networks","volume":"387","author":"Lambiotte","year":"2008","journal-title":"Phys. Stat. Mech. Its Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1111\/tgis.12042","article-title":"Discovering Spatial Interaction Communities from Mobile Phone Data","volume":"17","author":"Gao","year":"2013","journal-title":"Trans. GIS"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1111\/pirs.12149","article-title":"Uncovering regional characteristics from mobile phone data: A network science approach","volume":"95","author":"Chi","year":"2016","journal-title":"Pap. Reg. Sci."},{"key":"ref_17","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-Supervised Classification with Graph Convolutional Networks. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"20140378","DOI":"10.1098\/rsif.2014.0378","article-title":"The Matthew effect in empirical data","volume":"11","author":"Perc","year":"2014","journal-title":"J. R. Soc. Interface"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s003579900004","article-title":"Estimation and Prediction for Stochastic Blockmodels for Graphs with Latent Block Structure","volume":"14","author":"Snijders","year":"1997","journal-title":"J. Classif."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1080\/01621459.1987.10478385","article-title":"Stochastic Blockmodels for Directed Graphs","volume":"82","author":"Wang","year":"1987","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/0378-8733(83)90021-7","article-title":"Stochastic blockmodels: First steps","volume":"5","author":"Holland","year":"1983","journal-title":"Soc. Netw."},{"key":"ref_22","unstructured":"Ghalebi, E., Mirzasoleiman, B., Grosu, R., and Leskovec, J. (2018). Dynamic Network Model from Partial Observations. arXiv."},{"key":"ref_23","first-page":"292","article-title":"Find and evaluating community structure in networks","volume":"69","author":"Newman","year":"2004","journal-title":"Phys. Rev. E"},{"key":"ref_24","unstructured":"Montis, A.D., Barthelemy, M., Chessa, A., and Vespignani, A. (2005). The structure of Inter-Urban traffic: A weighted network analysis. Physics."},{"key":"ref_25","first-page":"1","article-title":"Nonparametric Network Models for Link Prediction","volume":"17","author":"Williamson","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sobolevsky, S., Szell, M., Campari, R., Couronn\u00e9, T., Smoreda, Z., and Ratti, C. (2013). Delineating geographical regions with networks of human interactions in an extensive set of countries. PLoS ONE.","DOI":"10.1371\/journal.pone.0081707"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Fortunato, S., and Lancichinetti, A. (2009, January 20\u201322). Community Detection Algorithms: A Comparative Analysis: Invited Presentation, Extended Abstract. Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools, Pisa, Italy.","DOI":"10.4108\/ICST.VALUETOOLS2009.8046"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fortunato, S. (2009). Community Detection in Graphs. arXiv.","DOI":"10.1007\/978-0-387-30440-3_76"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1073\/pnas.0706851105","article-title":"Maps of random walks on complex networks reveal community structure","volume":"105","author":"Rosvall","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s10707-017-0311-4","article-title":"The national geographic characteristics of online public opinion propagation in China based on WeChat network","volume":"22","author":"Ai","year":"2018","journal-title":"GeoInformatica"},{"key":"ref_31","unstructured":"Kemp, C., Tenenbaum, J.B., Griffiths, T.L., Yamada, T., and Ueda, N. (2006, January 16\u201320). Learning systems of concepts with an infinite relational model. Proceedings of the National Conference on Artificial Intelligence, Boston, MA, USA."},{"key":"ref_32","first-page":"4","article-title":"Method for evaluating credibility of domestic IP address library","volume":"105","author":"Song","year":"2014","journal-title":"Comput. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1080\/10618600.2000.10474879","article-title":"Markov Chain Sampling Methods for Dirichlet Process Mixture Models","volume":"9","author":"Neal","year":"2000","journal-title":"J. Comput. Graph. Stat."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1566","DOI":"10.1198\/016214506000000302","article-title":"Hierarchical Dirichlet Processes","volume":"101","author":"Teh","year":"2006","journal-title":"Publ. Am. Stat. Assoc."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/5\/290\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:32:22Z","timestamp":1760362342000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/5\/290"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,1]]},"references-count":34,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["ijgi9050290"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9050290","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2020,5,1]]}}}