Prepaid or Postpaid? That Is the Question: Novel Methods of Subscription Type Prediction in Mobile Phone Services

https://m2.mtmt.hu/ eng 2026-08-24 05:10 BookChapter 32842021 APPROVED true Funding Agency and Grant Number: SticAmSud UCOOL project; INRIA; SoSweet [ANR-15-CE38-0011-01]; CODDDE [ANR-13-CORD-0017-01] Funding text: We acknowledge Grandata to share the data and M. Fixman for his technical support. This research project was partially granted by the SticAmSud UCOOL project, INRIA, SoSweet (ANR-15-CE38-0011-01) and CODDDE (ANR-13-CORD-0017-01). 0 false 2026-05-21T22:17:09.809+0000 2022-05-26T08:45:27.775+0000 2022-05-25T10:06:03.541+0000 true 10015168 Szakonyi Erzsebet /api/admin/10015168 Admin Erzsebet Szakonyi (RAMKI admin 4) true 10015168 2022-06-09T11:14:04.738+0000 2022-06-09T11:14:04.738+0000 Admin 10014568 /api/admin/10014568 Andrea Vándor (VA, admin) Vándor Andrea true 10014568 true 2022-09-23T09:37:13.761+0000 true false true 25 25 /api/publicationtype/25 PublicationType Chapter in Book 2 true 25 BookChapter true 10000312 Conference paper true 25 25 /api/publicationtype/25 PublicationType Chapter in Book 2 true 25 BookChapter /api/subtype/10000312 Konferenciaközlemény SubType Conference paper (Chapter in Book) 228 true 10000312 true 1 /api/category/1 Category Scientific true 1 Liao, Yongjun Prepaid or Postpaid? That Is the Question: Novel Methods of Subscription Type Prediction in Mobile Phone Services 165 181 165 17 2018 In this paper, we investigate the behavioural differences between mobile phone customers with prepaid and postpaid subscriptions. Our study reveals that (a) postpaid customers are more active in terms of service usage and (b) there are strong structural correlations in the mobile phone call network as connections between customers of the same subscription type are much more frequent than those between customers of different subscription types. Based on these observations, we provide methods to detect the subscription type of customers by using information about their personal call statistics, and also their egocentric networks simultaneously. The key of our first approach is to cast this classification problem as a problem of graph labelling, which can be solved by max-flow min-cut algorithms. Our experiments show that, by using both user attributes and relationships, the proposed graph labelling approach is able to achieve a classification accuracy of similar to 87%, which outperforms by similar to 7% supervised learning methods using only user attributes. In our second problem, we aim to infer the subscription type of customers of external operators. We propose via approximate methods to solve this problem by using node attributes, and a two-way indirect inference method based on observed homophilic structural correlations. Our results have straightforward applications in behavioural prediction and personal marketing. 2022 true true true true false NONE 2026-05-22 false 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 false false false false 2022-08-24T08:45:27.513+0000 1 11 0 6 0 true true 10002 English /api/language/10002 Angol Language English true 2 false true 17370 true 10039 /api/country/10039 Country Austria true 2149310 /api/city/17370 City Wien, Bécs, Austria true /api/publication/30946067 Day MY. SOCIAL NETWORK BASED BIG DATA ANALYSIS AND APPLICATIONS. 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Yang J, 2013, IEEE DATA MINING, P1151, DOI 10.1109/ICDM.2013.167 1 false true Reference 31014992 /api/reference/31014992 2. Xu Z., 2012, P 2012 ACM SIGMOD IN, P505, DOI DOI 10.1145/2213836.2213894 2 false true Reference 31014991 /api/reference/31014991 3. Xu H., 2013, ADV KNOWLEDGE DISCOV 3 false true Reference 31014990 /api/reference/31014990 4. Tang J., 2012, P 5 ACM INT C WEB SE, P743, DOI [http://doi.acm.org/10.1145/2124295.2124382, DOI 10.1145/2124295.2124382] 4 false true Reference 31014989 /api/reference/31014989 5. Sun Y, 2012, PROC VLDB ENDOW, V5, P394, DOI 10.14778/2140436.2140437 5 false true Reference 31014988 /api/reference/31014988 6. Papadimitriou CH., 1982, COMBINATORIAL OPTIMI 6 false true Reference 31014987 /api/reference/31014987 7. Newman M, 2010, NETWORKS INTRO, V1 7 false true Reference 31014986 /api/reference/31014986 8. Malmi E., 2016, P 10 INT C WEB SOC M, P635 8 false true Reference 31014985 /api/reference/31014985 9. 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(2018) In: SOCIAL NETWORK BASED BIG DATA ANALYSIS AND APPLICATIONS pp. 165-181<div class="BookChapter Publication long-list"> <div class="authors"> <img title="Forrásközlemény" style="float: left" src="/frontend/resources/grid/publication-core-icon.png"> <div class="autype autype0"> <span class="author-name" >Liao Yongjun </span> ;    <span class="author-name" >Du Wei </span> ;    <span class="author-name" mtid="10015369"><a href="/gui2/?type=authors&mode=browse&sel=10015369" target="_blank">Karsai Marton ✉ (<span class="authorship-author-name">Karsai Márton</span> <span class="authorAux-mtmt"> Statisztikus fizika</span>) </a> </span> ;    <span class="author-name" >Sarraute Carlos </span> ;    <span class="author-name" >Minnoni Martin </span> ;    <span class="author-name" >Fleury Eric </span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;32842021" target="_blank">Prepaid or Postpaid? That Is the Question: Novel Methods of Subscription Type Prediction in Mobile Phone Services</a></div> <div class="InBook"><div class="chapter-in">In:</div> <div class="authors"> <div class="autype autype-1"> <span class="author-name" >Day MY </span> ;    <span class="author-name" >Khoury S </span> ;    <span class="author-name" >Kawash J </span> ;    <span class="author-name" >Kaya M </span> (eds.) </div> </div> <div class="booktitle"><a href="/gui2/?mode=browse&params=publication;30946067" target="_blank">SOCIAL NETWORK BASED BIG DATA ANALYSIS AND APPLICATIONS </a></div> <span class="publishedAt">Wien, Bécs: <span class="publishers">Springer Wien</span>, <span class="page"> pp 165-181 </span> <span class="year">(2018)</span> ( <span class="seriesTitle">Lecture Notes in Social Networks</span> <span class="issn">2190-5428</span> <span class="issn">2190-5436</span> ) </div> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Language: English | </span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:black" title="10.1007/978-3-319-78196-9_8" target="_blank" href="https://doi.org/10.1007/978-3-319-78196-9_8"> DOI </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="000460389600008" target="_blank" href="https://www.webofscience.com/wos/woscc/full-record/000460389600008"> WoS </a> </span> </span> <div class="mtid"><span class="long-pub-mtid">Publication: 32842021</span> | <span class="status-data status-APPROVED"> Published </span> <span class="long-book-mtid">Book: 30946067</span> Core | <span class="type-subtype">Chapter in Book ( Conference paper ) </span> | <span class="pub-category">Scientific</span> | <span class="publication-sourceOfData">WOS TAGGED</span> </div> <div class="lastModified">Last Modified: 2022.05.26. 10:45 Erzsebet Szakonyi (RAMKI admin 4) </div> <div class="lockedBy">Centrally managed 2022.09.23. 11:37 Andrea Vándor (VA, admin) </div> <pre class="comment" style="margin-top: 0; margin-bottom: 0;"><u>Comments</u>: Funding Agency and Grant Number: SticAmSud UCOOL project; INRIA; SoSweet [ANR-15-CE38-0011-01]; CODDDE [ANR-13-CORD-0017-01] Funding text: We acknowledge Grandata to share the data and M. Fixman for his technical support. This research project was partially granted by the SticAmSud UCOOL project, INRIA, SoSweet (ANR-15-CE38-0011-01) and CODDDE (ANR-13-CORD-0017-01).</pre> </div> </div>