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arXiv AI

ProTAGAD: A Foundation Model for TAG Anomaly Detection with Decoupled Topological and Textual Prototypes

arXiv:2608. 10699v1 Announce Type: cross Abstract: Text-Attributed Graphs (TAGs), endowed with abundant textual content along with topological structures, have emerged as a versatile backbone for real-world anomaly detection spanning large language model security, social network moderation, and cyber threat identification.

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arXiv Machine Learning
Sep 17

FoundAna: A GNN-assisted Foundation Model for Graph Anomaly Detection

FoundAna is a GNN‑assisted foundation model designed for graph anomaly detection across diverse datasets. It combines a GNN component with a transformer encoder enhanced by four positional encodings to capture both local and global structure, using reconstruction errors as anomaly scores. Experiments on nine benchmark datasets from financial, social, and citation networks show that FoundAna consistently outperforms state‑of‑the‑art baselines.

By Suprim Nakarmi, Chahana Dahal, Yue Zhao, Junggab Son, Zuobin Xiong
llmsbenchmarks
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arXiv Machine Learning
Sep 7

GLASS: Graph-Language Alignment with Spherical Scoring for Transferable Graph-Level Anomaly Detection

GLASS is a graph‑level anomaly detection framework that aligns graph and language representations on a unit hypersphere to achieve cross‑domain transferability. It constructs a Graph Descriptor Prompt to encode local, global, and semantic graph properties, and uses a multi‑slice soft cosine objective to unify graph and text embeddings. Anomaly scoring is performed via spherical density estimation with von Mises‑Fisher kernels, enabling zero‑shot detection and few‑shot adaptation across twelve benchmarks and three meta‑domains, outperforming recent GLAD baselines.

By Xudong Wang, Chris Ding, Tongxin Li, Jicong Fan
ragmultimodalbenchmarkssafety
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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
ragdiffusion
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arXiv Machine Learning
Sep 22

Clustering-Based Collective Anomaly Detection in IoT Systems: A Graph Neural Network Approach

The paper introduces Unsupervised Graph Collective Anomaly Detection (UGCAD), a framework that uses a variational graph autoencoder to learn graph representations of IoT network traffic and then enhances clustering to group nodes. UGCAD identifies collective anomalies by aggregating normal clusters and applying anomaly scores to the refined groups. Experiments on CICIoT2023 and ToN-IoT datasets show that UGCAD outperforms traditional and state‑of‑the‑art clustering‑based CAD methods in both clustering quality and anomaly detection accuracy.

By Dalila Khettaf, Djamel Djenouri, Zeinab Rezaeifar, Youcef Djenouri
benchmarks
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