Performance evaluation of social network anomaly detection using a moving window–based scan method
Timely detection of anomalous events in networks, particularly social networks, is a problem of increasing interest and relevance. A variety of methods have been proposed for monitoring such networks, including the window‐based scan method proposed by a previous study. However, research assessing the performance of this and other methods has been sparse. In this article, we use simulated social network structures to study the performance of the Priebe et al method. The detection power is high only when more than half of the social network experiences anomalous behavior or if the anomalous behavior is extreme. Both can be represented by high signal‐to‐noise ratios in the network. More precisely, Priebe's scan method performs well when the signal‐to‐noise ratio is above 20. Simulation studies are used to show that an improved detection rate and shortened monitoring delays can be achieved by lagging the moving window used for standardization, lowering the signaling threshold, and using shorter moving windows at the initial stage of monitoring. We suggest a community detection method to be used after an anomalous event has been identified to help determine the subnetwork associated with this anomalous behavior.
Publisher URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/qre.2364
Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.
Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.