GCTAM: Global and Contextual Truncated Affinity Combined Maximization Model For Unsupervised Graph Anomaly Detection

2026-03-02

AI Summary

This work addresses the limitations of existing graph anomaly detection methods based on Truncated Affinity Maximization (TAM), which rely on fixed thresholds and overlook node-specific characteristics and higher-order affinity relationships, leading to suboptimal truncation efficiency. To overcome these issues, we propose an adaptive truncation mechanism that integrates contextual and global affinity information within an unsupervised framework, dynamically suppressing affinities of anomalous nodes while enhancing those of normal nodes—thereby transcending the constraints of static thresholds. Extensive experiments on real-world large-scale datasets, including Amazon and YelpChi, demonstrate that our method outperforms state-of-the-art models by 15%–20% in detection performance. Moreover, it achieves superior results on ultra-large-scale datasets (Amazon-all and YelpChi-all), where most baseline approaches fail to scale effectively.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Graph-based Machine Learning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Anomalies often occur in real-world information networks/graphs, such as malevolent users, malicious comments, banned users, and fake news in social graphs. The latest graph anomaly detection methods use a novel mechanism called truncated affinity maximization (TAM) to detect anomaly nodes without using any label information and achieve impressive results. TAM maximizes the affinities among the normal nodes while truncating the affinities of the anomalous nodes to identify the anomalies. However, existing TAM-based methods truncate suspicious nodes according to a rigid threshold that ignores the specificity and high-order affinities of different nodes. This inevitably causes inefficient truncations from both normal and anomalous nodes, limiting the effectiveness of anomaly detection. To this end, this paper proposes a novel truncation model combining contextual and global affinity to truncate the anomalous nodes. The core idea of the work is to use contextual truncation to decrease the affinity of anomalous nodes, while global truncation increases the affinity of normal nodes. Extensive experiments on massive real-world datasets show that our method surpasses peer methods in most graph anomaly detection tasks. In highlights, compared with previous state-of-the-art methods, the proposed method has +15\% $\sim$ +20\% improvements in two famous real-world datasets, Amazon and YelpChi. Notably, our method works well in large datasets, Amazin-all and YelpChi-all, and achieves the best results, while most previous models cannot complete the tasks.

Problem

• graph anomaly detection
• truncated affinity maximization
• unsupervised learning
• node affinity
• anomaly identification

Innovation

• truncated affinity maximization
• graph anomaly detection
• contextual truncation
• global affinity
• unsupervised learning