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CGGM:a conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks

Munan Li Xianshi Su Runze Ma Tongbang Jiang Zijian Li Tony Q.S.Quek

数字通信与网络（英文）2026，Vol.12Issue(4)：686-697,12.

数字通信与网络（英文）2026，Vol.12Issue(4)：686-697,12.DOI:10.1016/j.dcan.2025.05.013

CGGM:a conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks

Munan Li ¹Xianshi Su ¹Runze Ma ¹Tongbang Jiang ¹Zijian Li ¹Tony Q.S.Quek²

作者信息

1. College of Artificial Intelligence,Dalian Maritime University,China
2. Information Systems Technology and Design,Singapore University of Technology and Design,Singapore||Yonsei Frontier Lab,Yonsei University,South Korea
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摘要

关键词

Anomaly detection/Graph neural network/Temporal graph embedding/Network traffic/Graph generation

Key words

Anomaly detection/Graph neural network/Temporal graph embedding/Network traffic/Graph generation

引用本文复制引用

Munan Li,Xianshi Su,Runze Ma,Tongbang Jiang,Zijian Li,Tony Q.S.Quek..CGGM:a conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks[J].数字通信与网络（英文）,2026,12(4):686-697,12.

基金项目

This work was supported by the Liaoning Provincial Natural Science Foundation(Grant No.2024-BS-015),and in part by China Postdoctoral Science Foundation(No.2024M750295). （Grant No.2024-BS-015）

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