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cs.LG2025
DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly Detection
Wenxin Zhang, Xiaojian Lin, Wenjun Yu +7
Time series anomaly detection holds notable importance for risk identification and fault detection across diverse application domains. Unsupervised learning methods have become pop…
cs.LG2025
Dual Boost-Driven Graph-Level Clustering Network
John Smith, Wenxuan Tu, Junlong Wu +10
Graph-level clustering remains a pivotal yet formidable challenge in graph learning. Recently, the integration of deep learning with representation learning has demonstrated notabl…
cs.LG2025
Multi-Relation Graph-Kernel Strengthen Network for Graph-Level Clustering
Renda Han, Guangzhen Yao, Wenxin Zhang +7
Graph-level clustering is a fundamental task of data mining, aiming at dividing unlabeled graphs into distinct groups. However, existing deep methods that are limited by pooling ha…