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However, existing methods are mostly unable to highlight the primary synergy relationship among nodes and consider much irrelevant information, which caused poor detectability. Therefore, this paper proposes a novel MRF-based method (ACEagle), considering node-level and community-level behavior features. Our method has several advantages: (1) based on the analysis of the nodes\u2019 local structure, the community-level behavioral features are combined to calculate the nodes\u2019 prior probability to close the ground truth, (2) it measured the behavior\u2019s collaborative intensity between nodes by time and weight, constructing MRF by the synergic relationship exceeding the threshold to filter irrelevant structural information, (3) it operates in a completely unsupervised fashion requiring no labeled data, while still incorporating side information if available. Through experiments in user-reviewed datasets where abnormal collusive behavior is most typical, the results show that ACEagle is significantly outperforming state-of-the-art baselines in collusive anomalies detection.<\/jats:p>","DOI":"10.3233\/ida-216287","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T11:34:56Z","timestamp":1667561696000},"page":"1469-1485","source":"Crossref","is-referenced-by-count":4,"title":["Collusive anomalies detection based on collaborative markov random field"],"prefix":"10.1177","volume":"26","author":[{"given":"Haoran","family":"Shi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lixin","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuxin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinxin","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-216287_ref1","doi-asserted-by":"crossref","unstructured":"J. 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