{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T22:53:56Z","timestamp":1753052036607,"version":"3.41.0"},"reference-count":73,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T00:00:00Z","timestamp":1677542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,7,31]]},"abstract":"<jats:p>Dense subtensor detection gains remarkable success in spotting anomalies and fraudulent behaviors for multi-aspect data (i.e., tensors), like in social media and event streams. Existing methods detect the densest subtensors flatly and separately, with the underlying assumption that those subtensors are exclusive. However, many real-world tensors usually present hierarchical properties, e.g., the core-periphery structure and dynamic communities in networks. It is also unexplored how to fuse the prior knowledge into dense pattern detection to capture the local behavior.<\/jats:p>\n          <jats:p>\n            In this article, we propose\n            <jats:sc>CatchCore<\/jats:sc>\n            , a novel framework to efficiently find the hierarchical dense subtensors. We first design a unified metric for dense subtensor detection, which can be optimized with gradient-based methods. With the proposed metric,\n            <jats:sc>CatchCore<\/jats:sc>\n            detects hierarchical dense subtensors through the hierarchy-wise alternative optimization and finds local dense patterns concerning some items in a query manner. Finally, we utilize the minimum description length principle to measure the quality of detection results and select the optimal hierarchical dense subtensors. Extensive experiments on synthetic and real-world datasets demonstrate that\n            <jats:sc>CatchCore<\/jats:sc>\n            outperforms the top competitors in accuracy for detecting dense subtensors and anomaly patterns, like network attacks. Additionally,\n            <jats:sc>CatchCore<\/jats:sc>\n            successfully identifies a hierarchical researcher co-authorship group with intense interactions in the DBLP dataset; it can also capture core collaboration and multi-hop relations around some query objects. Meanwhile,\n            <jats:sc>CatchCore<\/jats:sc>\n            also scales linearly with all aspects of tensors.\n          <\/jats:p>","DOI":"10.1145\/3577022","type":"journal-article","created":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T12:48:56Z","timestamp":1671540536000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Hierarchical Dense Pattern Detection in Tensors"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3636-0035","authenticated-orcid":false,"given":"Wenjie","family":"Feng","sequence":"first","affiliation":[{"name":"Institute of Data Science, National University of Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2120-3598","authenticated-orcid":false,"given":"Shenghua","family":"Liu","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Network Data Science andTechnology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5201-8195","authenticated-orcid":false,"given":"Xueqi","family":"Cheng","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Network Data Science andTechnology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2008.2008152"},{"key":"e_1_3_2_3_1","volume-title":"Proceedings of the 7th International Conference on Weblogs and Social Media","author":"Akoglu Leman","year":"2013","unstructured":"Leman Akoglu, Rishi Chandy, and Christos Faloutsos. 2013. 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