Abstract
The structure of the network in the real world is very complex, as the dynamic network structure evolves in time dimension, how to detect network changes accurately and further locate abnormal nodes is a research hotspot. Most current feature learning methods are difficult to capture a variety of network connectivity patterns, and have a high time complexity. In order to overcome this limitation, we introduce the network embedding method into the field of network change detection, we find that node-based egonet can better reflect the connectivity patterns of the node, so a dynamic network embedding model Egonet2Vec is proposed, which is based on extracting the connectivity patterns of the node-based egonets. After the dynamic network representation learning, we use a dynamic network change detection strategy to detect network change time points and locate abnormal nodes. We apply our method to real dynamic network datasets to demonstrate the validity of this method.
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Acknowledgment
This work was supported by the National Key R&D Program of China (No. 2016YFB0801303, 2016QY01W0105), the National Natural Science Foundation of China (No.61309007, U1636219, 61602508, 61772549, U1736214, 61572052) and Plan for Scientific Innovation Talent of Henan Province (No. 2018JR0018).
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Feng, H., Liu, Y., Zhou, Z., Chen, J. (2020). Dynamic Network Change Detection via Dynamic Network Representation Learning. In: Gao, H., Feng, Z., Yu, J., Wu, J. (eds) Communications and Networking. ChinaCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-41114-5_48
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DOI: https://doi.org/10.1007/978-3-030-41114-5_48
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