Temporal Graphs Anomaly Emergence Detection: Benchmarking For Social Media Interactions

TL;DR

This paper benchmarks 12 data-driven methods for anomaly detection in temporal graphs derived from social media, revealing the complexity and lack of a clear best approach for identifying emerging anomalies.

Contribution

It provides a comprehensive comparison of multiple methods on real social media data, highlighting challenges and gaps in current anomaly detection techniques for temporal graphs.

Findings

No clear best method identified for anomaly detection.

Challenges in detecting emerging anomalies in large, dynamic systems.

Highlights need for further research and innovative approaches.

Abstract

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.