CoANE: Modeling Context Co-occurrence for Attributed Network Embedding

I. Chung Hsieh, Cheng Te Li

研究成果: Conference contribution

摘要

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 https://ieeexplore.ieee.org/document/9431700.

原文English
主出版物標題Proceedings - 2022 IEEE 38th International Conference on Data Engineering, ICDE 2022
發行者IEEE Computer Society
頁面1567-1568
頁數2
ISBN(電子)9781665408837
DOIs
出版狀態Published - 2022
事件38th IEEE International Conference on Data Engineering, ICDE 2022 - Virtual, Online, Malaysia
持續時間: 2022 5月 92022 5月 12

出版系列

名字Proceedings - International Conference on Data Engineering
2022-May
ISSN(列印)1084-4627

Conference

Conference38th IEEE International Conference on Data Engineering, ICDE 2022
國家/地區Malaysia
城市Virtual, Online
期間22-05-0922-05-12

All Science Journal Classification (ASJC) codes

  • 軟體
  • 訊號處理
  • 資訊系統

指紋

深入研究「CoANE: Modeling Context Co-occurrence for Attributed Network Embedding」主題。共同形成了獨特的指紋。

引用此