ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks
Effectively detecting anomalous nodes in attributed networks is crucial for the success of many real-world applications such as fraud and intrusion detection. Existing approaches have difficulties with three major issues: sparsity and nonlinearity capturing, residual modeling, and network smoothing....
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| Published in | Machine learning Vol. 111; no. 2; pp. 519 - 541 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.02.2022
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0885-6125 1573-0565 |
| DOI | 10.1007/s10994-021-06044-0 |
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| Abstract | Effectively detecting anomalous nodes in attributed networks is crucial for the success of many real-world applications such as fraud and intrusion detection. Existing approaches have difficulties with three major issues: sparsity and nonlinearity capturing, residual modeling, and network smoothing. We propose Residual Graph Convolutional Network (ResGCN), an attention-based deep residual modeling approach that can tackle these issues: modeling the attributed networks with GCN allows to capture the sparsity and nonlinearity, utilizing a deep neural network allows direct residual ing from the input, and a residual-based attention mechanism reduces the adverse effect from anomalous nodes and prevents over-smoothing. Extensive experiments on several real-world attributed networks demonstrate the effectiveness of ResGCN in detecting anomalies. |
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| AbstractList | Effectively detecting anomalous nodes in attributed networks is crucial for the success of many real-world applications such as fraud and intrusion detection. Existing approaches have difficulties with three major issues: sparsity and nonlinearity capturing, residual modeling, and network smoothing. We propose Residual Graph Convolutional Network (ResGCN), an attention-based deep residual modeling approach that can tackle these issues: modeling the attributed networks with GCN allows to capture the sparsity and nonlinearity, utilizing a deep neural network allows direct residual ing from the input, and a residual-based attention mechanism reduces the adverse effect from anomalous nodes and prevents over-smoothing. Extensive experiments on several real-world attributed networks demonstrate the effectiveness of ResGCN in detecting anomalies. |
| Author | Pei, Yulong van Ipenburg, Werner Huang, Tianjin Pechenizkiy, Mykola |
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| CitedBy_id | crossref_primary_10_1109_ACCESS_2024_3398555 crossref_primary_10_32604_csse_2023_036352 crossref_primary_10_1016_j_ins_2023_03_022 crossref_primary_10_1016_j_knosys_2023_110589 crossref_primary_10_1007_s10618_023_00960_6 crossref_primary_10_1109_JSYST_2023_3347435 crossref_primary_10_1109_TKDE_2024_3501307 crossref_primary_10_1109_TMM_2023_3312931 crossref_primary_10_1007_s13042_024_02211_6 crossref_primary_10_1080_09540091_2022_2078281 crossref_primary_10_1007_s11517_024_03273_y crossref_primary_10_1016_j_neucom_2024_127761 crossref_primary_10_1109_ACCESS_2022_3211306 crossref_primary_10_1109_TGRS_2022_3145474 crossref_primary_10_1007_s00521_023_08964_5 crossref_primary_10_1109_TCE_2024_3355122 crossref_primary_10_1109_TKDE_2023_3250523 crossref_primary_10_1016_j_dsm_2024_09_002 crossref_primary_10_1016_j_eswa_2025_127311 crossref_primary_10_1016_j_knosys_2025_113144 crossref_primary_10_1007_s11036_024_02339_3 crossref_primary_10_1007_s11053_024_10448_9 crossref_primary_10_1109_ACCESS_2022_3201332 |
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| SubjectTerms | Anomalies Artificial Intelligence Artificial neural networks Computer Science Control Fraud Machine Learning Mechatronics Modelling Natural Language Processing (NLP) Nodes Nonlinearity Robotics Simulation and Modeling Smoothing Sparsity Special Issue: Foundations of Data Science |
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| Title | ResGCN: attention-based deep residual modeling for anomaly detection on attributed networks |
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