TY - GEN
T1 - Understanding the Influence of Extremely High-Degree Nodes on Graph Anomaly Detection
AU - Sun, Xun
AU - Xiao, Xi
AU - Wang, Zhaoguo
AU - Hu, Guangwu
AU - Jiang, Xuhui
AU - Zhang, Bin
AU - Li, Hao
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Graph Anomaly Detection (GAD) has attracted considerable attention for its potential in detecting anomalies. However, an overlooked issue in prior research is the presence of extremely high-degree node, which can introduce noise into GAD, escalate computational costs, and intensify the problem of over-smoothing. To tackle this issue, this paper first presents a novel graph anomaly dataset, NFTGraph, characterized by a notable presence of extremely high-degree nodes. A series of experiments on this dataset sheds light on the influence of such nodes on GAD. Moreover, we introduce a novel model, the Super Node-Aware Graph Neural Network (SNGNN), designed to mitigate the noise emanating from extremely high-degree nodes. Experimental results demonstrate that SNGNN outperforms extant models, achieving an average improvement of over 2% in the Area Under the ROC Curve (AUROC), and effectively reducing noise.
AB - Graph Anomaly Detection (GAD) has attracted considerable attention for its potential in detecting anomalies. However, an overlooked issue in prior research is the presence of extremely high-degree node, which can introduce noise into GAD, escalate computational costs, and intensify the problem of over-smoothing. To tackle this issue, this paper first presents a novel graph anomaly dataset, NFTGraph, characterized by a notable presence of extremely high-degree nodes. A series of experiments on this dataset sheds light on the influence of such nodes on GAD. Moreover, we introduce a novel model, the Super Node-Aware Graph Neural Network (SNGNN), designed to mitigate the noise emanating from extremely high-degree nodes. Experimental results demonstrate that SNGNN outperforms extant models, achieving an average improvement of over 2% in the Area Under the ROC Curve (AUROC), and effectively reducing noise.
KW - Extremely High-degree Nodes
KW - Graph Anomaly Detection
UR - https://www.scopus.com/pages/publications/85212273169
U2 - 10.1007/978-3-031-78183-4_2
DO - 10.1007/978-3-031-78183-4_2
M3 - 会议稿件
AN - SCOPUS:85212273169
SN - 9783031781827
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 19
EP - 34
BT - Pattern Recognition - 27th International Conference, ICPR 2024, Proceedings
A2 - Antonacopoulos, Apostolos
A2 - Chaudhuri, Subhasis
A2 - Chellappa, Rama
A2 - Liu, Cheng-Lin
A2 - Bhattacharya, Saumik
A2 - Pal, Umapada
PB - Springer Science and Business Media Deutschland GmbH
T2 - 27th International Conference on Pattern Recognition, ICPR 2024
Y2 - 1 December 2024 through 5 December 2024
ER -