基于多尺度跨模态与单模态对比学习的文本属性图异常检测
Text-Attributed Graph Anomaly Detection via Multi-Scale Cross- and Uni-Modal Contrastive Learning
AI总结：
针对文本属性图异常检测中文本编码与检测目标脱节的问题，提出端到端范式CMUCL，通过多尺度跨模态与单模态对比学习联合训练编码器，结合不一致性挖掘估计异常分数，性能显著优于现有方法，并发布8个专用基准数据集。
摘要：
图数据在各类高风险场景中的广泛应用提升了人们对图异常检测（GAD）的关注。现实世界中的图通常带有以原始文本序列形式存在的节点描述，这类图被称为文本属性图（TAGs）；现有的图异常检测流程通常采用浅层嵌入技术将此类文本信息编码为特征，再依赖图域内复杂的自监督任务检测异常。然而，该文本编码过程与图域的异常检测训练目标相互分离，难以确保提取的文本特征聚焦于与图异常检测相关的信息，严重制约了检测能力。如何无缝融合原始文本与图拓扑结构，释放文本属性图中跨模态数据在异常检测方面的巨大潜力，是一个颇具挑战性的问题。本文提出了一种面向文本属性图异常检测的新型端到端范式，名为CMUCL。我们同时对文本和图结构数据建模，利用跨模态与单模态的多尺度一致性联合训练文本编码器和图编码器，以挖掘潜在的异常相关信息。据此，我们设计了一种基于不一致性挖掘的异常分数估计器，用于计算每个节点的专属异常分数。考虑到目前缺乏专为文本属性图异常检测定制的基准数据集，我们发布了8个数据集以推动未来研究。大量评估实验表明，CMUCL在文本属性图异常检测任务上性能显著提升，相比次优方法的平均精度（AP）提升了11.13%。
英文摘要：
The widespread application of graph data in various high-risk scenarios has increased attention to graph anomaly detection (GAD). Faced with real-world graphs that often carry node descriptions in the form of raw text sequences, termed text-attributed graphs (TAGs), existing graph anomaly detection pipelines typically involve shallow embedding techniques to encode such textual information into features, and then rely on complex self-supervised tasks within the graph domain to detect anomalies. However, this text encoding process is separated from the anomaly detection training objective in the graph domain, making it difficult to ensure that the extracted textual features focus on GAD-relevant information, seriously constraining the detection capability. How to seamlessly integrate raw text and graph topology to unleash the vast potential of cross-modal data in TAGs for anomaly detection poses a challenging issue. This paper presents a novel end-to-end paradigm for text-attributed graph anomaly detection, named CMUCL. We simultaneously model data from both text and graph structures, and jointly train text and graph encoders by leveraging cross-modal and uni-modal multi-scale consistency to uncover potential anomaly-related information. Accordingly, we design an anomaly score estimator based on inconsistency mining to derive node-specific anomaly scores. Considering the lack of benchmark datasets tailored for anomaly detection on TAGs, we release 8 datasets to facilitate future research. Extensive evaluations show that CMUCL significantly advances in text-attributed graph anomaly detection, delivering an 11.13% increase in average accuracy (AP) over the suboptimal.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
