Title | Towards Robust Dynamic Network Embedding |
Author | |
Publication Years | 2021
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ISSN | 1045-0823
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Source Title | |
Pages | 4889-4890
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Abstract | Dynamic Network Embedding (DNE) has recently drawn much attention due to the dynamic nature of many real-world networks. Comparing to a static network, a dynamic network has a unique character called the degree of changes, which can be defined as the average number of the changed edges between consecutive snapshots spanning a dynamic network. The degree of changes could be quite different even for the dynamic networks generated from the same dataset. It is natural to ask whether existing DNE methods are effective and robust w.r.t. the degree of changes. Towards robust DNE, we suggest two important scenarios. One is to investigate the robustness w.r.t. different slicing settings that are used to generate different dynamic networks with different degree of changes, while another focuses more on the robustness w.r.t. different number of changed edges over timesteps. |
SUSTech Authorship | First
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Language | English
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URL | [Source Record] |
Indexed By | |
EI Accession Number | 20220911734874
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EI Keywords | Artificial intelligence
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ESI Classification Code | Artificial Intelligence:723.4
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Scopus EID | 2-s2.0-85125466356
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Data Source | Scopus
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Document Type | Conference paper |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/406301 |
Department | Department of Computer Science and Engineering |
Affiliation | 1.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China 2.School of Computer Science,University of Birmingham,Birmingham,United Kingdom |
First Author Affilication | Department of Computer Science and Engineering |
First Author's First Affilication | Department of Computer Science and Engineering |
Recommended Citation GB/T 7714 |
Hou,Chengbin,Tang,Ke. Towards Robust Dynamic Network Embedding[C],2021:4889-4890.
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