CoANE: Modeling Context Co-occurrence for Attributed Network Embedding

I. Chung Hsieh, Cheng Te Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Attributed network embedding (ANE) is to learn low-dimensional vectors so that not only the network structure but also node attributes can be preserved in embeddings. Existing ANE models do not consider the specific combination between graph structure and attributes. While each node has its structural characteristics, such as highly-interconnected neighbors along with their certain patterns of attribute distribution, each node's neighborhood should be not only depicted by multi-hop nodes, but consider certain social circles. To model such information, in this paper, we propose a novel ANE model, Context Co-occurrence-aware Attributed Network Embedding (CoANE). The basic idea of CoANE is to model the context attributes for each node, and apply the convolutional mechanism to encode latent social circles. To better preserve network knowledge, we devise objective functions including positive graph likelihood, contextual negative sampling, and attribute reconstruction. We conduct experiments on five real datasets for downstream tasks, including node classification, link prediction, and community detection. The results exhibit that CoANE can significantly outperform state-of-the-art ANE models. This work was accepted to IEEE TKDE11Full paper is available at

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE 38th International Conference on Data Engineering, ICDE 2022
PublisherIEEE Computer Society
Number of pages2
ISBN (Electronic)9781665408837
Publication statusPublished - 2022
Event38th IEEE International Conference on Data Engineering, ICDE 2022 - Virtual, Online, Malaysia
Duration: 2022 May 92022 May 12

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Conference38th IEEE International Conference on Data Engineering, ICDE 2022
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems


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