Towards Robust Dynamic Network Embedding

Hou，Chengbin1,2; Tang，Ke1

2021

1045-0823

IJCAI International Joint Conference on Artificial Intelligence

4889-4890

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.

First

English

EI

20220911734874

Artificial intelligence

Artificial Intelligence:723.4

2-s2.0-85125466356

Scopus

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

Hou，Chengbin,Tang，Ke. Towards Robust Dynamic Network Embedding[C],2021:4889-4890.