Sep 2026· World wide web (Bussum)· Vol 29· 0 citations· 38 references
Advanced Graph Neural Networks
TL;DR
Multi-View Semi-Supervised Graph Anomaly Detection (MSGAD) is introduced, an innovative framework tailored for detecting anomalies in dynamic graph environments that enhances robustness against noise and unexpected events while improving sensitivity to subtle anomalous signals distributed across time.
BAD is proposed, an unsupervised framework for anomaly detection in continuous-time dynamic graphs that represents nodes with learnable identity embeddings and performs pairwise compatibility modeling via cross-attention between each destination node and the source’s recent neighbors, enabling direct characterization o...
Jia-Chi Luo, Sha-Meng Wen, Zi-Yan Qiu et al.· Proceedings of the Thirty-Fi...· 0 citations
A novel framework, Generate and Filter graph learning for Graph Anomaly Detection (GFGAD), which generates a diverse set of synthetic anomalies with enriched feature and structural information to balance the data distribution and significantly outperforms state-of-the-art baselines.
Meng-Yu Li, Yong-Hao Liu, Xi-Ming Li et al.· IEEE Transactions on Pattern...· 0 citations
LTRGAD is proposed, a two-stage GAD framework that performs feature selection based on local feature-topological residuals (LTR) and effectively introduces topological information while preserving the original local anomalous patterns, enabling more accurate local anomaly detection.
Ya-Zheng Zhao, Nan-Nan Wu, Hao Yin et al.· Proceedings of the Thirty-Fi...· 0 citations
Unsupervised multivariate time-series anomaly detection seeks to detect rare abnormal events in correlated sensor streams when dense anomaly labels are unavailable. Although Transformer-based methods have strengthened long-range representation learning, many still depend on fixed patch construction or costly attention...
Jun Long, Xu-Zhuang Yan, Jun-Kun Hong et al.· Journal of King Saud Univers...· 0 citations
Online test-time adaptation for generalizable DGAD is formulated and a proposed OTTA-DGAD, which selectively retains general patterns shared across the source domains used for pretraining while incorporating new patterns from the target domain.
Jialun Zheng, Han-Chen Yang, Jiannong Cao et al.· 0 citations
Graph-level anomaly detection (GLAD) aims to identify graphs that deviate from the majority in a dataset of graphs. Existing methods typically adopt either a global aggregation perspective that summarizes nodes within a graph into a representation vector, or a subgraph-oriented perspective which regards certain local s...
Ge Zhang, Hui-Mei Li, Guo-Hao Sun et al.· Proceedings of the Thirty-Fi...· 0 citations
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