中文版 | English
Title

Towards Robust Dynamic Network Embedding

Author
Publication Years
2021
ISSN
1045-0823
Source Title
Pages
4889-4890
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
Language
English
URL[Source Record]
Indexed By
EI Accession Number
20220911734874
EI Keywords
Artificial intelligence
ESI Classification Code
Artificial Intelligence:723.4
Scopus EID
2-s2.0-85125466356
Data Source
Scopus
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/406301
DepartmentDepartment 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 AffilicationDepartment of Computer Science and Engineering
First Author's First AffilicationDepartment 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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