Temporal graphs have become an essential tool for analyzing complex dynamic systems with multiple agents. Detecting anomalies in temporal graphs is crucial for various applications, including identifying emerging trends, monitoring network security, understanding social dynamics, tracking disease outbreaks, and understanding financial dynamics. In this paper, we present a comprehensive benchmarking study that compares 12 data-driven methods for anomaly detection in temporal graphs. We conduct experiments on two temporal graphs extracted from Twitter and Facebook, aiming to identify anomalies in group interactions. Surprisingly, our study reveals an unclear pattern regarding the best method for such tasks, highlighting the complexity and challenges involved in anomaly emergence detection in large and dynamic systems. The results underscore the need for further research and innovative approaches to effectively detect emerging anomalies in dynamic systems represented as temporal graphs.

翻译：时序图已成为分析多智能体复杂动态系统的重要工具。检测时序图中的异常对于识别新兴趋势、监控网络安全、理解社会动态、追踪疾病爆发和分析金融动态等应用至关重要。本文提出了一项全面的基准研究，比较了12种基于数据驱动的方法在时序图异常检测中的表现。我们在从Twitter和Facebook提取的两个时序图上开展实验，旨在识别群体交互中的异常。令人惊讶的是，我们的研究揭示了关于此类任务最优方法的不明确模式，凸显了大规模动态系统中异常涌现检测所涉及的复杂性与挑战。这些结果强调了对动态系统（以时序图表示）中有效检测新兴异常进行进一步研究与创新方法的必要性。