Proceedings

Towards Robust Dynamic Network Embedding

Chengbin Hou, Ke Tang

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 4889-4890.

https://doi.org/10.24963/ijcai.2021/676

Keywords

  • Natural Language Processing: Embeddings
  • Data Mining: Mining Graphs, Semi Structured Data, Complex Data
  • Machine Learning: Time-series; Data Streams
  • Machine Learning: Ensemble Methods

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.

Chengbin Hou, and Ke Tang. 2021. Towards Robust Dynamic Network Embedding. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4889–4890. https://doi.org/10.24963/ijcai.2021/676
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