{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T20:20:36Z","timestamp":1783110036725,"version":"3.54.6"},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF CAREER","award":["1452425"],"award-info":[{"award-number":["1452425"]}]},{"name":"PwC Risk and Regulatory Services Innovation Center"},{"DOI":"10.13039\/100008047","name":"Carnegie Mellon University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100008047","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,2,28]]},"abstract":"<jats:p>\n            Within a large database \ud835\udca2 containing graphs with labeled nodes and directed, multi-edges; how can we detect the anomalous graphs? Most existing work are designed for plain (unlabeled) and\/or simple (unweighted) graphs. We introduce\n            <jats:sc>CODEtect<\/jats:sc>\n            , the\n            <jats:italic>first<\/jats:italic>\n            approach that addresses the anomaly detection task for graph databases with such complex nature. To this end, it identifies a small representative set \ud835\udcae of structural patterns (i.e., node-labeled network motifs) that losslessly compress database \ud835\udca2 as concisely as possible. Graphs that do not compress well are flagged as anomalous.\n            <jats:sc>CODEtect<\/jats:sc>\n            exhibits two novel building blocks: (i) a motif-based lossless graph encoding scheme, and (ii) fast memory-efficient search algorithms for \ud835\udcae. We show the effectiveness of\n            <jats:sc>CODEtect<\/jats:sc>\n            on transaction graph databases from three different corporations and statistically similar synthetic datasets, where existing baselines adjusted for the task fall behind significantly, across different types of anomalies and performance metrics.\n          <\/jats:p>","DOI":"10.1145\/3533770","type":"journal-article","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T11:19:26Z","timestamp":1651663166000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Detecting Anomalous Graphs in Labeled Multi-Graph Databases"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3463-7232","authenticated-orcid":false,"given":"Hung T.","family":"Nguyen","sequence":"first","affiliation":[{"name":"Princeton University, Princeton, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9246-3269","authenticated-orcid":false,"given":"Pierre J.","family":"Liang","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3026-5731","authenticated-orcid":false,"given":"Leman","family":"Akoglu","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,2,20]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Leman Akoglu Mary McGlohon and Christos Faloutsos. 2010. Oddball: Spotting anomalies in weighted graphs. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining . Springer 410\u2013421.","DOI":"10.1007\/978-3-642-13672-6_40"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-014-0365-y"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Leman Akoglu Hanghang Tong Jilles Vreeken and Christos Faloutsos. 2012. Fast and reliable anomaly detection in categorical data. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management . 415\u2013424.","DOI":"10.1145\/2396761.2396816"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1226"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1088\/1751-8113\/41\/22\/224001"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371788"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aad9029"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00691-y"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00691-y"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.5555\/1618595.1618605"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.67"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.5555\/1368018.1368024"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783413"},{"key":"e_1_3_2_15_2","doi-asserted-by":"crossref","unstructured":"Dhivya Eswaran Christos Faloutsos Sudipto Guha and Nina Mishra. 2018. SpotLight: Detecting anomalies in streaming graphs. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM 1378\u20131386.","DOI":"10.1145\/3219819.3220040"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219862"},{"key":"e_1_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Aditya Grover and Jure Leskovec. 2016. Node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 855\u2013864.","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/4643.001.0001"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF02523693"},{"key":"e_1_3_2_20_2","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems . 1024\u20131034."},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3056563"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-10-318"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bth163"},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Danai Koutra U. Kang Jilles Vreeken and Christos Faloutsos. 2014. VOG: Summarizing and understanding large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems . SIAM 91\u201399.","DOI":"10.1137\/1.9781611973440.11"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/2011\/11\/P11005"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/2133360.2133363"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498473"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3118815"},{"key":"e_1_3_2_29_2","doi-asserted-by":"crossref","unstructured":"Emaad A. Manzoor Sadegh M. Milajerdi and Leman Akoglu. 2016. Fast memory-efficient anomaly detection in streaming heterogeneous graphs. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM 1035\u20131044.","DOI":"10.1145\/2939672.2939783"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1089167"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.298.5594.824"},{"key":"e_1_3_2_32_2","unstructured":"Annamalai Narayanan Mahinthan Chandramohan Rajasekar Venkatesan Lihui Chen Yang Liu and Shantanu Jaiswal. 2017. graph2vec: Learning distributed representations of graphs. arxiv:1707.05005. Retrieved from https:\/\/arxiv.org\/abs\/1707.05005."},{"key":"e_1_3_2_33_2","doi-asserted-by":"crossref","unstructured":"Saket Navlakha Rajeev Rastogi and Nisheeth Shrivastava. 2008. Graph summarization with bounded error. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data . ACM 419\u2013432.","DOI":"10.1145\/1376616.1376661"},{"key":"e_1_3_2_34_2","first-page":"2014","volume-title":"International Conference on Machine Learning","author":"Niepert Mathias","year":"2016","unstructured":"Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016. Learning convolutional neural networks for graphs. In International Conference on Machine Learning. 2014\u20132023."},{"key":"e_1_3_2_35_2","doi-asserted-by":"crossref","unstructured":"Caleb C. Noble and Diane J. Cook. 2003. Graph-based anomaly detection. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM 631\u2013636.","DOI":"10.1145\/956750.956831"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3018661.3018731"},{"key":"e_1_3_2_37_2","volume-title":"MLG 2020: 16th International Workshop on Mining and Learning with Graphs","author":"Paudel Ramesh","year":"2020","unstructured":"Ramesh Paudel and William Eberle. 2020. SNAPSKETCH: Graph representation approach for intrusion detection in a streaming graph. In MLG 2020: 16th International Workshop on Mining and Learning with Graphs. ACM."},{"key":"e_1_3_2_38_2","article-title":"Graph convolutional neural networks via motif-based attention","author":"Peng Hao","year":"2018","unstructured":"Hao Peng, Jianxin Li, Qiran Gong, Senzhang Wang, Yuanxing Ning, and Philip S. Yu. 2018. Graph convolutional neural networks via motif-based attention. arXiv:1811.08270. Retrieved from https:\/\/arxiv.org\/abs\/1811.08270.","journal-title":"arXiv:1811.08270"},{"key":"e_1_3_2_39_2","doi-asserted-by":"crossref","unstructured":"Bryan Perozzi and Leman Akoglu. 2016. Scalable anomaly ranking of attributed neighborhoods. In Proceedings of the 2016 SIAM International Conference on Data Mining . SIAM 207\u2013215.","DOI":"10.1137\/1.9781611974348.24"},{"key":"e_1_3_2_40_2","doi-asserted-by":"crossref","unstructured":"Bryan Perozzi Leman Akoglu Patricia Iglesias S\u00e1nchez and Emmanuel M\u00fcller. 2014. Focused clustering and outlier detection in large attributed graphs. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 1346\u20131355.","DOI":"10.1145\/2623330.2623682"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1016\/0005-1098(78)90005-5"},{"key":"e_1_3_2_42_2","unstructured":"Ryan A. Rossi Nesreen K. Ahmed Aldo G. Carranza David Arbour Anup Rao Sungchul Kim and Eunyee Koh. 2019. Heterogeneous network motifs. arxiv:1901.10026. Retrieved from https:\/\/arxiv.org\/abs\/1901.10026."},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220097"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308560.3320092"},{"key":"e_1_3_2_45_2","doi-asserted-by":"crossref","unstructured":"Neil Shah Danai Koutra Tianmin Zou Brian Gallagher and Christos Faloutsos. 2015. TimeCrunch: Interpretable dynamic graph summarization. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM 1055\u20131064.","DOI":"10.1145\/2783258.2783321"},{"key":"e_1_3_2_46_2","first-page":"488","volume-title":"Proceedings of the Artificial Intelligence and Statistics","author":"Shervashidze Nino","year":"2009","unstructured":"Nino Shervashidze, S. V. N. Vishwanathan, Tobias Petri, Kurt Mehlhorn, and Karsten M. Borgwardt. 2009. Efficient graphlet kernels for large graph comparison. In Proceedings of the Artificial Intelligence and Statistics. 488\u2013495."},{"key":"e_1_3_2_47_2","doi-asserted-by":"crossref","unstructured":"Kijung Shin Tina Eliassi-Rad and Christos Faloutsos. 2016. Corescope: Graph mining using k-core analysis\u2013patterns anomalies and algorithms. In 2016 IEEE 16th International Conference on Data Mining . IEEE 469\u2013478.","DOI":"10.1109\/ICDM.2016.0058"},{"key":"e_1_3_2_48_2","doi-asserted-by":"crossref","unstructured":"Koen Smets and Jilles Vreeken. 2011. The odd one out: Identifying and characterising anomalies. In Proceedings of the 2011 SIAM International Conference on Data Mining . SIAM\/Omnipress 804\u2013815.","DOI":"10.1137\/1.9781611972818.69"},{"key":"e_1_3_2_49_2","doi-asserted-by":"crossref","unstructured":"Nikolaj Tatti and Jilles Vreeken. 2008. Finding good itemsets by packing data. In Proceedings of the 2008 8th IEEE International Conference on Data Mining . IEEE Computer Society 588\u2013597.","DOI":"10.1109\/ICDM.2008.39"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/1376616.1376675"},{"key":"e_1_3_2_51_2","doi-asserted-by":"crossref","unstructured":"Johan Ugander Lars Backstrom and Jon M. Kleinberg. 2013. Subgraph frequencies: Mapping the empirical and extremal geography of large graph collections. In Proceedings of the 22nd international conference on World Wide Web Daniel Schwabe Virg\u00edlio A. F. Almeida Hartmut Glaser Ricardo A. Baeza-Yates and Sue B. Moon (Eds.). ACM 1307\u20131318.","DOI":"10.1145\/2488388.2488502"},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0406024101"},{"key":"e_1_3_2_53_2","doi-asserted-by":"crossref","unstructured":"Jilles Vreeken Matthijs van Leeuwen and Arno Siebes. 2011. Krimp: Mining itemsets that compress. Data Mining and Knowledge Discovery 23 1 (2011) 169\u2013214.","DOI":"10.1007\/s10618-010-0202-x"},{"key":"e_1_3_2_54_2","volume-title":"Proceedings of the International Conference on Learning Representations.","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783417"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.97.052306"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1089\/big.2021.0069"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411979"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3533770","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3533770","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T18:43:41Z","timestamp":1750272221000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3533770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,20]]},"references-count":57,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,2,28]]}},"alternative-id":["10.1145\/3533770"],"URL":"https:\/\/doi.org\/10.1145\/3533770","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,20]]},"assertion":[{"value":"2021-08-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-04-26","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}