arXiv Machine Learning June 8, 2026 By Xudong Wang, Ziheng Sun, Chris Ding, Jicong Fan

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection

Read the original on arXiv Machine Learning →

arXiv:2505. 21285v5 Announce Type: replace Abstract: This work proposes a framework LGKDE that learns kernel density estimation for graphs.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction

arXiv:2606. 12673v1 Announce Type: cross Abstract: Cross-domain graph anomaly detection (GAD) aims to identify abnormal nodes in unseen target graphs, showing strong potential in real-world applications with heterogeneous graph data.

By Phan Nguyen, Dat Cao, Hien Chu, Khue Hoang

arXiv Machine Learning

Jun 2

AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks

arXiv:2606. 01283v1 Announce Type: new Abstract: Modeling spatial dependencies is central to spatiotemporal data analysis using Graph Neural Networks (GNNs).

By Zhongyue Zhang, Guangyin Jin, Yuxuan Liang, Suwan Yin, Yuankai Wu

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Jun 2

Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection

arXiv:2606. 00304v1 Announce Type: new Abstract: Graph anomaly detection methods aim to distinguish anomalous nodes.

By Yilin Liu, Hongchao Zhang, Taylor T. Johnson, Ahmad F. Taha, Meiyi Ma

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AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders

arXiv:2511. 17113v3 Announce Type: replace-cross Abstract: Network Intrusion Detection Systems (NIDS) are essential tools for detecting network attacks and intrusions.

By Georgios Anyfantis, Pere Barlet-Ros

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Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection

arXiv:2505. 18934v2 Announce Type: replace-cross Abstract: Graph Anomaly Detection (GAD) in heterogeneous networks presents unique challenges due to node and edge heterogeneity.

By Xiping Li, Xiangyu Dong, Xingyi Zhang, Kun Xie, Yuanhao Feng, Bo Wang, Guilin Li, Wuxiong Zeng, Xiujun Shu, Sibo Wang

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arXiv Machine Learning

Jun 9

Normality Calibration in Semi-supervised Graph Anomaly Detection

arXiv:2510. 02014v3 Announce Type: replace Abstract: Graph anomaly detection (GAD) has attracted growing interest for its crucial ability to uncover irregular patterns in broad applications.

By Guolei Zeng, Hezhe Qiao, Guoguo Ai, Jinsong Guo, Guansong Pang

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Related stories

A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction

AdaKernel: Learning Adaptive Kernel Parameters for Spatiotemporal Graph Neural Networks

Modeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection

AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoders

Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection

Normality Calibration in Semi-supervised Graph Anomaly Detection