{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T09:38:40Z","timestamp":1742981920984,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789813349216"},{"type":"electronic","value":"9789813349223"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T00:00:00Z","timestamp":1611014400000},"content-version":"vor","delay-in-days":384,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Network community detection is an important service provided by social networks, and social network user location can greatly improve the quality of community detection. Label propagation is one of the main methods to realize the user location prediction. The traditional label propagation algorithm has the problems including \u201clocation label countercurrent\u201d and the update randomness of node location label, which seriously affects the accuracy of user location prediction. In this paper, a new location prediction algorithm for social networks based on improved label propagation algorithm is proposed. By computing the K-hop public neighbor of any two point in the social network graph, the nodes with the maximal similarity and their K-hopping neighbors are merged to constitute the initial label propagation set. The degree of nodes not in the initial set are calculated. The node location labels are updated asynchronously is adopted during the iterative process, and the node with the largest degree is selected to update the location label. The improvement proposed solves the \u201clocation label countercurrent\u201d and reduces location label updating randomness. The experimental results show that the proposed algorithm improves the accuracy of position prediction and reduces the time cost compared with the traditional algorithms.<\/jats:p>","DOI":"10.1007\/978-981-33-4922-3_12","type":"book-chapter","created":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T11:21:04Z","timestamp":1610968864000},"page":"165-174","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Label Propagation Based User Locations Prediction Algorithm in Social Network"],"prefix":"10.1007","author":[{"given":"Huan","family":"Ma","sequence":"first","affiliation":[]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,19]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Caverlee, J., Lee, K.: You are where you tweet: a content-based approach to geo-locating Twitter users. In: The 19th ACM Conference on Information and Knowledge Management, pp. 759\u2013768. ACM, Toronto (2010)","DOI":"10.1145\/1871437.1871535"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Yuan, Q., Cong, G., Ma, Z., et al.: Who, where, when and what: discover spatio-temporal topics for Twitter users. In: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 605\u2013613. ACM, Chicago (2013)","DOI":"10.1145\/2487575.2487576"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Noulas, A., Scellato, S., Lathia, N., et al.: Mining user mobility features for next place prediction in location-based services. In: 13th Industrial Conference on Data Mining, pp. 1038\u20131043, IEEE, New York (2013)","DOI":"10.1109\/ICDM.2012.113"},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Rakesh, V., Reddy, C.K., Singh, D., et al.: Location-specific tweet detection and topic summarization in Twitter. In: Proceedings of the 2013 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1441\u20131444. ACM, Niagara (2013)","DOI":"10.1145\/2492517.2492583"},{"key":"12_CR5","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1016\/j.procs.2014.05.321","volume":"31","author":"J Ao","year":"2014","unstructured":"Ao, J., Zhang, P., Cao, Y.: Estimating the locations of emergency events from Twitter streams. Procedia Comput. Sci. 31, 731\u2013739 (2014)","journal-title":"Procedia Comput. Sci."},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Lingad, J., Karimi, S., Yin, J.: Location extraction from disaster-related microblogs. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1017\u20131020. ACM, Rio de Janeiro (2013)","DOI":"10.1145\/2487788.2488108"},{"issue":"1","key":"12_CR7","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1109\/TKDE.2013.42","volume":"26","author":"O Van Laere","year":"2014","unstructured":"Van Laere, O., Quinn, J., Schockaert, S., et al.: Spatially aware term selection for geotagging. IEEE Trans. Knowl. Data Eng. 26(1), 221\u2013234 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"12_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1007\/978-3-642-35341-3_13","volume-title":"Information Retrieval Technology","author":"K Ren","year":"2012","unstructured":"Ren, K., Zhang, S., Lin, H.: Where are you settling down: geo-locating Twitter users based on tweets and social networks. In: Hou, Y., Nie, J.-Y., Sun, L., Wang, B., Zhang, P. (eds.) AIRS 2012. LNCS, vol. 7675, pp. 150\u2013161. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-35341-3_13"},{"key":"12_CR9","unstructured":"Han, B., Cook, P., Baldwin, T.: Geolocation prediction in social media data by finding location indicative words. In: 24th International Conference on Computational Linguistics, pp. 1045\u20131062. ACM, Mumbai (2012)"},{"key":"12_CR10","unstructured":"Mahmud, J., Nichols, J., Drews, C.: Where is this tweet from? Inferring home locations of Twitter users. In: Sixth International AAAI Conference on Weblogs and Social Media, pp. 73\u201377. AAAI, Dublin (2012)"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Backstrom, L., Kleinberg, J., Kumar, R., et al.: Spatial variation in search engine queries. In: Proceedings of the 17th International Conference on World Wide Web, pp. 357\u2013366. ACM, Beijing (2008)","DOI":"10.1145\/1367497.1367546"},{"key":"12_CR12","doi-asserted-by":"crossref","unstructured":"Backstrom, L., Sun, E., Marlow, C.: Find me if you can: improving geographical prediction with social and spatial proximity. In: Proceedings of the 19th International Conference on World Wide Web, pp. 61\u201370. ACM, North Carolina (2010)","DOI":"10.1145\/1772690.1772698"},{"issue":"13","key":"12_CR13","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.14778\/2733004.2733060","volume":"7","author":"L Kong","year":"2014","unstructured":"Kong, L., Liu, Z., Huang, Y.: SPOT: locating social media users based on social network context. Proc. VLDB Endow. 7(13), 1681\u20131684 (2014)","journal-title":"Proc. VLDB Endow."},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Li, R., Wang, S., Deng, H., Wang, R., Chang, K.C.: Towards social user profiling: unified and discriminative influence model for inferring home locations. In: The 18th International ACM SIGKDD Conference, pp. 1023\u20131031. ACM, Beijing (2012)","DOI":"10.1145\/2339530.2339692"},{"key":"12_CR15","unstructured":"Davis, Jr C., Pappa, G., de Oliveira, D., de L Arcanjo, F.: Inferring the location of twitter messages based on user relationships. Trans. GIS 15(6), 735\u2013751 (2011)"},{"key":"12_CR16","unstructured":"Jurgens, D.: That\u2019s what friends are for: inferring location in online social media platforms based on social relationships. In: Seventh International AAAI Conference on Weblogs and Social Media, pp. 237\u2013240. AAAI, Massachusetts (2013)"},{"issue":"11","key":"12_CR17","doi-asserted-by":"publisher","first-page":"1603","DOI":"10.14778\/2350229.2350273","volume":"5","author":"R Li","year":"2012","unstructured":"Li, R., Wang, S., Chang, C.: Multiple location profiling for users and relationships from social network and content. Proc. VLDB Endow. 5(11), 1603\u20131614 (2012)","journal-title":"Proc. VLDB Endow."}],"container-title":["Communications in Computer and Information Science","Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-33-4922-3_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T11:32:08Z","timestamp":1610969528000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-33-4922-3_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9789813349216","9789813349223"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-33-4922-3_12","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"19 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CNCERT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China Cyber Security Annual Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Beijing","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cncert2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/conf.cert.org.cn","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}