Abstract
Recently, there is a surge of research interests in finding anomalous nodes upon attributed networks. However, a vast majority of existing methods fail to capture the evolution of the networks properly, as they regard them as static. Meanwhile, they treat all the attributes and the instances equally, ignoring the existence of noisy. To tackle these problems, we propose a novel dynamic anomaly detection framework based on residual analysis, namely AMAD. It leverages the small smooth disturbance between time stamps to characterize the evolution of networks for incrementally update. Experiments conducted on several datasets show the superiority of AMAD in detecting anomalies.
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Acknowledgements
This work is supported by National Key Research and Development Program of China (2016YFB1000903), National Nature Science Foundation of China (61872287, 61532015 and 61672418), Innovative Research Group of the National Natural Science Foundation of China (61721002), Innovation Research Team of Ministry of Education (IRT_17R86), Project of China Knowledge Center for Engineering Science and Technology.
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Xue, L., Luo, M., Peng, Z., Li, J., Chen, Y., Liu, J. (2019). Anomaly Detection in Time-Evolving Attributed Networks. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_19
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DOI: https://doi.org/10.1007/978-3-030-18590-9_19
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