arXiv：cs.LG（机器学习，全量分类）· Xudong Wang, Chris Ding, Tongxin Li, Jicong Fan·· 15 小时前AI 评分33
GLASS：基于球面评分与图-语言对齐的可迁移图级异常检测框架
GLASS: Graph-Language Alignment with Spherical Scoring for Transferable Graph-Level Anomaly Detection
AI 导读
GLASS 通过在多维单位超球面上对齐结构感知图编码器与指令感知文本嵌入，实现跨域可迁移的图级异常检测。该框架将图的局部、全局与语义属性序列化为 GraphDP 提示词，并以 Spherical Multi-Modal Scoring 在球面上做密度估计，支持零样本与少样本适配。在十二个基准、三个元域上，GLASS 的平均 AUROC 与排名均优于近期先进 GLAD 基线。
正文
Abstract:We introduce GLASS, a framework for graph-level anomaly detection (GLAD) that achieves robust cross-domain transferability through graph-language alignment on the unit hypersphere. GLASS builds a unified representation space by aligning a structure-aware graph encoder with an instruction-aware text embedding via a multi-slice soft cosine objective. Our framework serializes local, global, and semantic graph properties into a compact Graph Descriptor Prompt (GraphDP), creating a text bridge that enables domain-agnostic anomaly scoring. By enforcing multi-scale consistency through Matryoshka representation slices, the model captures anomalous deviations at multiple levels of granularity. We formulate anomaly detection as density estimation on the aligned hypersphere and introduce Spherical Multi-Modal Scoring (SMS), which instantiates von Mises-Fisher kernel density estimators in both graph and text embedding spaces. This probabilistic formulation recovers angular 1-nearest-neighbor scoring in the high-concentration limit, motivates the practical mean k-nearest-neighbor scorer, and provides a principled fusion of structural and semantic anomaly signals. The shared text embedding space further serves as a cross-domain bridge: by encoding a target domain's GraphDP without target-domain training data, GLASS performs zero-shot anomaly detection, and with only a handful of normal examples, few-shot adaptation via reference-set calibration. For privacy-sensitive deployment, we extend reference-set calibration with a bounded joint graph-text kernel summary that provides graph-record differential privacy while keeping the encoders fixed independently of the private target references. Across twelve benchmarks and three meta-domains, GLASS obtains the best average AUROC and rank compared with recent advanced GLAD baselines and enables effective cross-domain transfer.
| Comments: | This work and project were done in Apr. 2026. This work was included in Xudong Wang's Ph.D. thesis (Defense Passed on 13 Apr. 2026), "Principled and Effective Graph Representation Learning with Application to Anomaly Detection," deposited with The Chinese University of Hong Kong, Shenzhen Library |
| Subjects: | Machine Learning (cs.LG) |
| ACM classes: | I.2.6; I.5.2 |
| Cite as: | arXiv:2609.05253 [cs.LG] |
| (or arXiv:2609.05253v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.05253 arXiv-issued DOI via DataCite |
Submission history
From: Xudong Wang [view email]
[v1]
Fri, 4 Sep 2026 15:16:16 UTC (6,208 KB)
[v2]
Thu, 1 Oct 2026 14:13:58 UTC (6,246 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org