{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T19:54:15Z","timestamp":1774986855172,"version":"3.50.1"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031144622","type":"print"},{"value":"9783031144639","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-14463-9_10","type":"book-chapter","created":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T12:02:48Z","timestamp":1660132968000},"page":"150-167","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Identifying Fraud Rings Using Domain Aware Weighted Community Detection"],"prefix":"10.1007","author":[{"given":"Shaik","family":"Masihullah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meghana","family":"Negi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jose","family":"Matthew","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jairaj","family":"Sathyanarayana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,8,11]]},"reference":[{"key":"10_CR1","unstructured":"Bandyopadhyay, S., Peter, V.: Unsupervised constrained community detection via self-expressive graph neural network. In: Uncertainty in Artificial Intelligence, pp. 1078\u20131088. PMLR (2021)"},{"key":"10_CR2","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/978-3-030-37720-5_3","volume-title":"Mining Data for Financial Applications","author":"R Van Belle","year":"2020","unstructured":"Van Belle, R., Mitrovi\u0107, S., De Weerdt, J.: Representation learning in graphs for credit card fraud detection. In: Bitetta, V., Bordino, I., Ferretti, A., Gullo, F., Pascolutti, S., Ponti, G. (eds.) MIDAS 2019. LNCS (LNAI), vol. 11985, pp. 32\u201346. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-37720-5_3"},{"issue":"10","key":"10_CR3","doi-asserted-by":"publisher","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","volume":"2008","author":"VD Blondel","year":"2008","unstructured":"Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Statist. Mech. Theory Exp. 2008(10), P10008 (2008)","journal-title":"J. Statist. Mech. Theory Exp."},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Cao, C., Li, S., Yu, S., Chen, Z.: Fake reviewer group detection in online review systems. In: 2021 International Conference on Data Mining Workshops (ICDMW), pp. 935\u2013942. IEEE (2021)","DOI":"10.1109\/ICDMW53433.2021.00122"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"10_CR6","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1016\/j.neucom.2017.08.035","volume":"275","author":"G Collell","year":"2018","unstructured":"Collell, G., Prelec, D., Patil, K.R.: A simple plug-in bagging ensemble based on threshold-moving for classifying binary and multiclass imbalanced data. Neurocomputing 275, 330\u2013340 (2018)","journal-title":"Neurocomputing"},{"issue":"5","key":"10_CR7","first-page":"1","volume":"1695","author":"G Csardi","year":"2006","unstructured":"Csardi, G., Nepusz, T., et al.: The igraph software package for complex network research. Int. J. Complex Syst. 1695(5), 1\u20139 (2006)","journal-title":"Int. J. Complex Syst."},{"key":"10_CR8","unstructured":"Data61, C.: Stellargraph machine learning library (2018). https:\/\/github.com\/stellargraph\/stellargraph"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H., Yu, P.S.: Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 315\u2013324 (2020)","DOI":"10.1145\/3340531.3411903"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Ghosh, S., et al.: Distributed Louvain algorithm for graph community detection. In: 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 885\u2013895. IEEE (2018)","DOI":"10.1109\/IPDPS.2018.00098"},{"key":"10_CR11","unstructured":"Hagberg, A., Swart, P., S Chult, D.: Exploring network structure, dynamics, and function using network. Technical report, Los Alamos National Lab. (LANL), Los Alamos, NM (United States) (2008)"},{"key":"10_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113024","volume":"142","author":"A Hashemi","year":"2020","unstructured":"Hashemi, A., Dowlatshahi, M.B., Nezamabadi-Pour, H.: MGFS: a multi-label graph-based feature selection algorithm via PageRank centrality. Expert Syst. App. 142, 113024 (2020)","journal-title":"Expert Syst. App."},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"He, D., Song, Y., Jin, D., Feng, Z., Zhang, B., Yu, Z., Zhang, W.: Community-centric graph convolutional network for unsupervised community detection. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 3515\u20133521 (2021)","DOI":"10.24963\/ijcai.2020\/486"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Jia, Y., Zhang, Q., Zhang, W., Wang, X.: Communitygan: community detection with generative adversarial nets. In: The World Wide Web Conference, pp. 784\u2013794 (2019)","DOI":"10.1145\/3308558.3313564"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Li, A., Qin, Z., Liu, R., Yang, Y., Li, D.: Spam review detection with graph convolutional networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2703\u20132711 (2019)","DOI":"10.1145\/3357384.3357820"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Liang, C., Liu, Z., Liu, B., Zhou, J., Li, X., Yang, S., Qi, Y.: Uncovering insurance fraud conspiracy with network learning. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1181\u20131184 (2019)","DOI":"10.1145\/3331184.3331372"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Y., et al.: Pick and choose: a GNN-based imbalanced learning approach for fraud detection. In: Proceedings of the Web Conference 2021, pp. 3168\u20133177 (2021)","DOI":"10.1145\/3442381.3449989"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Dou, Y., Yu, P.S., Deng, Y., Peng, H.: Alleviating the inconsistency problem of applying graph neural network to fraud detection. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1569\u20131572 (2020)","DOI":"10.1145\/3397271.3401253"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Z., Chen, C., Yang, X., Zhou, J., Li, X., Song, L.: Heterogeneous graph neural networks for malicious account detection. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 2077\u20132085 (2018)","DOI":"10.1145\/3269206.3272010"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Luo, L., Fang, Y., Cao, X., Zhang, X., Zhang, W.: Detecting communities from heterogeneous graphs: A context path-based graph neural network model. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 1170\u20131180 (2021)","DOI":"10.1145\/3459637.3482250"},{"issue":"21","key":"10_CR21","doi-asserted-by":"publisher","first-page":"433","DOI":"10.21105\/joss.00433","volume":"3","author":"LJ Miranda","year":"2018","unstructured":"Miranda, L.J.: PySwarms: a research toolkit for particle swarm optimization in python. J. Open Source Softw. 3(21), 433 (2018)","journal-title":"J. Open Source Softw."},{"issue":"6","key":"10_CR22","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.69.066133","volume":"69","author":"ME Newman","year":"2004","unstructured":"Newman, M.E.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6), 066133 (2004)","journal-title":"Phys. Rev. E"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Nilforoshan, H., Shah, N.: Slicendice: mining suspicious multi-attribute entity groups with multi-view graphs. In: 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 351\u2013363. IEEE (2019)","DOI":"10.1109\/DSAA.2019.00050"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Noekhah, S., binti Salim, N., Zakaria, N.H.: Opinion spam detection: using multi-iterative graph-based model. Inf. Process. Manage. 57(1), 102140 (2020)","DOI":"10.1016\/j.ipm.2019.102140"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Peng, L., Lin, R.: Fraud phone calls analysis based on label propagation community detection algorithm. In: 2018 IEEE World Congress on Services (SERVICES), pp. 23\u201324. IEEE (2018)","DOI":"10.1109\/SERVICES.2018.00025"},{"key":"10_CR26","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1016\/j.swevo.2017.10.009","volume":"39","author":"S Rahimi","year":"2018","unstructured":"Rahimi, S., Abdollahpouri, A., Moradi, P.: A multi-objective particle swarm optimization algorithm for community detection in complex networks. Swarm Evol. Comput. 39, 297\u2013309 (2018)","journal-title":"Swarm Evol. Comput."},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Rayana, S., Akoglu, L.: Collective opinion spam detection: bridging review networks and metadata. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 985\u2013994 (2015)","DOI":"10.1145\/2783258.2783370"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Sarma, D., Alam, W., Saha, I., Alam, M.N., Alam, M.J., Hossain, S.: Bank fraud detection using community detection algorithm. In: 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA), pp. 642\u2013646. IEEE (2020)","DOI":"10.1109\/ICIRCA48905.2020.9182954"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Shang, C., Tang, Y., Huang, J., Bi, J., He, X., Zhou, B.: End-to-end structure-aware convolutional networks for knowledge base completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3060\u20133067 (2019)","DOI":"10.1609\/aaai.v33i01.33013060"},{"key":"10_CR30","unstructured":"Shchur, O., G\u00fcnnemann, S.: Overlapping community detection with graph neural networks. arXiv preprint arXiv:1909.12201 (2019)"},{"issue":"16","key":"10_CR31","doi-asserted-by":"publisher","first-page":"7179","DOI":"10.3390\/app11167179","volume":"11","author":"S Souravlas","year":"2021","unstructured":"Souravlas, S., Anastasiadou, S., Katsavounis, S.: A survey on the recent advances of deep community detection. Appl. Sci. 11(16), 7179 (2021)","journal-title":"Appl. Sci."},{"key":"10_CR32","doi-asserted-by":"publisher","first-page":"13589","DOI":"10.1109\/ACCESS.2018.2887119","volume":"7","author":"C Sun","year":"2018","unstructured":"Sun, C., Yan, Z., Li, Q., Zheng, Y., Lu, X., Cui, L.: Abnormal group-based joint medical fraud detection. IEEE Access 7, 13589\u201313596 (2018)","journal-title":"IEEE Access"},{"issue":"1","key":"10_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-41695-z","volume":"9","author":"VA Traag","year":"2019","unstructured":"Traag, V.A., Waltman, L., Van Eck, N.J.: From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9(1), 1\u201312 (2019)","journal-title":"Sci. Rep."},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Wang, D., et al.: A semi-supervised graph attentive network for financial fraud detection. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 598\u2013607. IEEE (2019)","DOI":"10.1109\/ICDM.2019.00070"},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Wang, L., Li, P., Xiong, K., Zhao, J., Lin, R.: Modeling heterogeneous graph network on fraud detection: a community-based framework with attention mechanism. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 1959\u20131968 (2021)","DOI":"10.1145\/3459637.3482277"},{"issue":"6","key":"10_CR36","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1007\/s13042-019-01042-0","volume":"11","author":"G Yang","year":"2019","unstructured":"Yang, G., Zheng, W., Che, C., Wang, W.: Graph-based label propagation algorithm for community detection. Int. J. Mach. Learn. Cybern. 11(6), 1319\u20131329 (2019). https:\/\/doi.org\/10.1007\/s13042-019-01042-0","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.06.030","volume":"187","author":"X You","year":"2020","unstructured":"You, X., Ma, Y., Liu, Z.: A three-stage algorithm on community detection in social networks. Knowl. Based Syst. 187, 104822 (2020)","journal-title":"Knowl. Based Syst."},{"key":"10_CR38","doi-asserted-by":"crossref","unstructured":"Zhang, S., Yin, H., Chen, T., Hung, Q.V.N., Huang, Z., Cui, L.: GCN-based user representation learning for unifying robust recommendation and fraudster detection. In: Proceedings of the 43rd international ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 689\u2013698 (2020)","DOI":"10.1145\/3397271.3401165"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-14463-9_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:03:41Z","timestamp":1709831021000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-14463-9_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031144622","9783031144639"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-14463-9_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 August 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CD-MAKE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Cross-Domain Conference for Machine Learning and Knowledge Extraction","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vienna","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cd-make2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cd-make.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"45","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}