{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:27:39Z","timestamp":1760243259717,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2014,5,14]],"date-time":"2014-05-14T00:00:00Z","timestamp":1400025600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Online auctions attract not only legitimate businesses trying to sell their products but also fraudsters wishing to commit fraudulent transactions. Consequently, fraudster detection is crucial to ensure the continued success of online auctions. This paper proposes an approach to detect fraudsters based on the concept of neighbor diversity. The neighbor diversity of an auction account quantifies the diversity of all traders that have transactions with this account. Based on four different features of each trader (i.e., the number of received ratings, the number of cancelled transactions, k-core, and the joined date), four measurements of neighbor diversity are proposed to discern fraudsters from legitimate traders. An experiment is conducted using data gathered from a real world auction website. The results show that, although the use of neighbor diversity on k-core or on the joined date shows little or no improvement in detecting fraudsters, both the neighbor diversity on the number of received ratings and the neighbor diversity on the number of cancelled transactions improve classification accuracy, compared to the state-of-the-art methods that use k-core and center weight.<\/jats:p>","DOI":"10.3390\/e16052629","type":"journal-article","created":{"date-parts":[[2014,5,14]],"date-time":"2014-05-14T11:22:27Z","timestamp":1400066547000},"page":"2629-2641","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Using Neighbor Diversity to Detect Fraudsters in Online Auctions"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6844-1182","authenticated-orcid":false,"given":"Jun-Lin","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Information Management, Yuan Ze University, 135 Yuan-Tung Road, Chungli, Taoyuan 32003, Taiwan"},{"name":"Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laksamee","family":"Khomnotai","sequence":"additional","affiliation":[{"name":"Department of Information Management, Yuan Ze University, 135 Yuan-Tung Road, Chungli, Taoyuan 32003, Taiwan"},{"name":"Faculty of Management Science, Nakhon Ratchasima Rajabhat University, Nakhon Ratchasima 30000, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2014,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1111\/j.1530-9134.2006.00103.x","article-title":"Reputation in Auctions: Theory, and Evidence from eBay","volume":"15","author":"Houser","year":"2006","journal-title":"J. Econ. Manag. Strategy"},{"key":"ref_2","unstructured":"Wang, J.-C., and Chiu, C.-Q. Available online: http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.69.6139&rep=rep1&type=pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s00453-007-9106-6","article-title":"New algorithms for mining the reputation of participants of online auctions","volume":"52","author":"Morzy","year":"2008","journal-title":"Algorithmica"},{"key":"ref_4","unstructured":"Chau, D.H., and Faloutsos, C. (2005, January 3). Fraud detection in electronic auction, Porto, Portugal."},{"key":"ref_5","first-page":"103","article-title":"Detecting Fraudulent Personalities in Networks of Online Auctioneers","volume":"4213","author":"Scheffer","year":"2006","journal-title":"Knowledge Discovery in Databases: PKDD 2006"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1016\/j.eswa.2007.01.045","article-title":"Recommending trusted online auction sellers using social network analysis","volume":"34","author":"Wang","year":"2008","journal-title":"Expert Syst. Appl"},{"key":"ref_7","unstructured":"Pandit, S., Chau, D.H., Wang, S., and Faloutsos, C. Netprobe: A fast and scalable system for fraud detection in online auction networks."},{"key":"ref_8","unstructured":"Bin, Z., Yi, Z., and Faloutsos, C. (2008, January 7\u201310). Toward a Comprehensive Model in Internet Auction Fraud Detection, Waikoloa, Big Island, HI, USA."},{"key":"ref_9","first-page":"279","article-title":"Cluster-based analysis and recommendation of sellers in online auctions","volume":"22","author":"Morzy","year":"2007","journal-title":"Comput. Syst. Sci. Eng"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9079","DOI":"10.1016\/j.eswa.2012.02.039","article-title":"Combining ranking concept and social network analysis to detect collusive groups in online auctions","volume":"39","author":"Lin","year":"2012","journal-title":"Expert Syst. Appl"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s10257-010-0135-3","article-title":"Web crawling and filtering for on-line auctions from a social network perspective","volume":"10","author":"Yu","year":"2012","journal-title":"Inf. Syst. E Bus. Manag"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s10660-013-9113-4","article-title":"Fuzzy rule optimization for online auction frauds detection based on genetic algorithm","volume":"13","author":"Yu","year":"2013","journal-title":"Electron. Commer. Res"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"123","DOI":"10.2753\/JEC1086-4415150306","article-title":"Internet Auction Fraud Detection Using Social Network Analysis and Classification Tree Approaches","volume":"15","author":"Chiu","year":"2011","journal-title":"Int. J. Electron. Commer"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1002\/j.1538-7305.1948.tb00917.x","article-title":"A Mathematical Theory of Communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J"},{"key":"ref_15","unstructured":"Quinlan, J.R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"117","DOI":"10.3390\/e13010117","article-title":"Mean-variance-skewness-entropy measures: A multi-objective approach for portfolio selection","volume":"13","author":"Usta","year":"2011","journal-title":"Entropy"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4607","DOI":"10.3390\/e15114607","article-title":"On the Diversity Constraints for Portfolio Optimization","volume":"15","author":"Lin","year":"2013","journal-title":"Entropy"},{"key":"ref_18","first-page":"41","article-title":"E-Auction Frauds\u2014A Survey","volume":"61","author":"Noufidali","year":"2013","journal-title":"Int. J. Comput. Appl"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.cosrev.2009.09.001","article-title":"Combating online in-auction fraud: Clues, techniques and challenges","volume":"3","author":"Dong","year":"2009","journal-title":"Comput. Sci. Rev"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1287\/mnsc.1070.0747","article-title":"The sound of silence in online feedback: Estimating trading risks in the presence of reporting bias","volume":"54","author":"Dellarocas","year":"2008","journal-title":"Manag. Sci"},{"key":"ref_21","first-page":"33","article-title":"Online Reputation Systems: How to Design One That Does What You Need","volume":"51","author":"Dellarocas","year":"2010","journal-title":"MIT Sloan Manag. Rev"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.elerap.2011.06.001","article-title":"Reputation inflation detection in a Chinese C2C market","volume":"10","author":"You","year":"2011","journal-title":"Electron. Commer. Res. Appl"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.elerap.2012.02.005","article-title":"An effective early fraud detection method for online auctions","volume":"11","author":"Chang","year":"2012","journal-title":"Electron. Commer. Res. Appl"},{"key":"ref_24","unstructured":"Batagelj, V., and Zaversnik, M. (2002). An O(m) Algorithm for Cores Decomposition of Networks, arXiv, cs\/0310049."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1016\/j.eswa.2007.01.045","article-title":"Recommending Trusted Online Auction Sellers Using Social Network Analysis","volume":"34","author":"Wang","year":"2008","journal-title":"Expert Syst. Appl"},{"key":"ref_26","unstructured":"Witten, I.H., Frank, E., and Hall, M.A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishers Inc."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/16\/5\/2629\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:11:23Z","timestamp":1760217083000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/16\/5\/2629"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,5,14]]},"references-count":26,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2014,5]]}},"alternative-id":["e16052629"],"URL":"https:\/\/doi.org\/10.3390\/e16052629","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2014,5,14]]}}}