arXiv Machine Learning

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

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

arXiv AI

Jun 12

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

arXiv Machine Learning

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

benchmarks

arXiv AI

Jul 13

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

arXiv AI

Jul 22

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

benchmarks safety

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

safety

arXiv Machine Learning

Jul 31

ARES: Anomaly Recognition Model For Edge Streams

arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.

By Simone Mungari, Albert Bifet, Giuseppe Manco, Bernhard Pfahringer

rag diffusion

arXiv AI

Jun 8

Graph Neural Network leveraging Higher-order Class Label Connectivity for Heterophilous Graphs

arXiv:2606. 07475v1 Announce Type: cross Abstract: Node classification in graph neural networks (GNNs) has been widely applied in various fields of graph analysis.

By Takuto Takahashi, Itsuki Nakayama, Takahiro Mitani, Ryosuke Kikuchi, Yuya Sasaki, Makoto Onizuka

rag benchmarks

arXiv Machine Learning

Jul 28

Unsupervised Graph Representation Learning with Complementary View Alignment

arXiv:2607. 24338v1 Announce Type: new Abstract: Unsupervised graph representation learning aims to derive meaningful node embeddings by capturing both structural and attribute information without relying on labeled data.

By Zengyi Wo, Shiyu Zhang, Qiyao Peng, Tianpeng Li, Xuan Guo

rag benchmarks safety

arXiv Machine Learning

Jun 3

Topology-Aware Gaussian Graph Repair for Robust Graph Neural Networks

arXiv:2606. 03462v1 Announce Type: new Abstract: Graph neural networks have achieved strong performance on graph-structured data, but their effectiveness depends heavily on the quality of the observed graph.

By Anubha Goel, Juho Kanniainen

benchmarks

arXiv AI

Jun 2

Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision

arXiv:2602. 20019v2 Announce Type: replace-cross Abstract: Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies.

By Yuxing Tian, Yiyan Qi, Fengran Mo, Weixu Zhang, Jian Guo, Jian-Yun Nie

arXiv Machine Learning

Jun 16

Anomaly Detection via Mean Shift Density Enhancement

arXiv:2602. 03293v2 Announce Type: replace Abstract: Unsupervised anomaly detection stands as an important problem in machine learning.

By Pritam Kar, Rahul Bordoloi, Olaf Wolkenhauer, Saptarshi Bej

benchmarks

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