Web: http://arxiv.org/abs/2205.04816

May 11, 2022, 1:11 a.m. | Jiaqiang Zhang, Senzhang Wang, Songcan Chen

cs.LG updates on arXiv.org arxiv.org

Detecting abnormal nodes from attributed networks is of great importance in
many real applications, such as financial fraud detection and cyber security.
This task is challenging due to both the complex interactions between the
anomalous nodes with other counterparts and their inconsistency in terms of
attributes. This paper proposes a self-supervised learning framework that
jointly optimizes a multi-view contrastive learning-based module and an
attribute reconstruction-based module to more accurately detect anomalies on
attributed networks. Specifically, two contrastive learning views are …

anomaly detection arxiv detection learning networks on

More from arxiv.org / cs.LG updates on arXiv.org

Data & Insights Strategy & Innovation General Manager

@ Chevron Services Company, a division of Chevron U.S.A Inc. | Houston, TX

Faculty members in Research areas such as Bayesian and Spatial Statistics; Data Privacy and Security; AI/ML; NLP; Image and Video Data Analysis

@ Ahmedabad University | Ahmedabad, India

Director, Applied Mathematics & Computational Research Division

@ Lawrence Berkeley National Lab | Berkeley, Ca

Business Data Analyst

@ MainStreet Family Care | Birmingham, AL

Assistant/Associate Professor of the Practice in Business Analytics

@ Georgetown University McDonough School of Business | Washington DC

Senior Data Science Writer

@ NannyML | Remote