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RGSE: Robust Graph Structure Embedding for Anomalous Link Detection
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DOI：10.1109/TBDATA.2023.3284270.png)
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摘要
En 中文
Anomalous links such as noisy links or adversarial edges widely exist in real-world networks, which may undermine the credibility of the network study, e.g., community detection in social networks. Therefore, anomalous links need to be removed from the polluted network by a detector. Due to the co-existence of normal links and anomalous links, how to identify anomalous links in a polluted network is a challenging issue. By designing a robust graph structure embedding framework, also called RGSE, the link-level feature representations that are generated from both global embedding view and local stable view can be used for anomalous link detection on contaminated graphs. Comparison experiments on a variety of datasets demonstrate that the new model and its variants achieve up to an average 5.2% improvement with respect to the accuracy of anomalous link detection against the traditional graph representation models. Further analyses also provide interpretable evidence to support the model's superiority.
Keyword:
Big Data
Convolutional neural networks
Computational modeling
Task analysis
Social networking (online)
Noise measurement
Natural language processing
Anomalous link detection
auto-encoder
dual-view-based framework
robust graph structure embedding
期刊
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5.7
论文数:
887
被引数:
3.0K
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