{"type":"ScholarlyArticle","title":"Accelerated Local Anomaly Detection via Resolving Attributed Networks","authors":[{"name":"Ninghao Liu","slug":"ninghao-liu","given":"Ninghao","family":"Liu","affiliation":"Department of Computer Science and Engineering , Texas A&M University"},{"name":"Xiao Huang","slug":"xiao-huang","given":"Xiao","family":"Huang","affiliation":"Department of Computer Science and Engineering , Texas A&M University"},{"name":"Xia Hu","slug":"xia-hu","given":"Xia","family":"Hu","affiliation":"Department of Computer Science and Engineering , Texas A&M University; Center for Remote Health Technologies and Systems , Texas A&M Engineering Experiment Station"}],"abstract":"Attributed networks, in which network connectivity and node attributes are available, have been increasingly used to model real-world information systems, such as social media and e-commerce platforms. While outlier detection has been extensively studied to identify anomalies that deviate from certain chosen background, existing algorithms cannot be directly applied on attributed networks due to the heterogeneous types of information and the scale of real-world data. Meanwhile, it has been observed that  local anomalies, which may align with global condition, are hard to be detected by existing algorithms with interpretability. Motivated by the observations, in this paper, we propose to study the problem of effective and efficient local anomaly detection in attributed networks. In particular, we design a collective way for modeling heterogeneous network and attribute information, and develop a novel and efficient distributed optimization algorithm to handle large-scale data. In the experiments, we compare the proposed framework with the state-of-the-art methods on both real and synthetic datasets, and demonstrate its effectiveness and efficiency through quantitative evaluation and case studies.","doi":"10.24963/ijcai.2017/325","url":"https://ijcai.org/proceedings/2017/325","pdf":"https://ijcai.org/proceedings/2017/0325.pdf","markdown":"https://ijcai.org/proceedings/2017/325.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/325","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":325,"pages":{"first":2337,"last":2343},"keywords":["Machine Learning: Unsupervised Learning","Multidisciplinary Topics and Applications: AI and Social Sciences","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Akoglu et al., 2012] L. 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