Size agnostic Change Point Detection (CPD) framework
- Ossi Mokryn
- Apr 20, 2020
- 2 min read
Inference statistic helps determine points of change in the evolution of networks; The framework does not use historic data nor imposes size restrictions, and is best suited to interaction networks, also termed temporal asynchronous human communication networks.
Paper: Hadar Miller, Osnat Mokryn. Size Agnostic Change Point Detection Framework. March 2020, PLoS ONE 15(4), pp. 1-23, e0231035. (Link)
Changes in the structure of observed social and complex networks can indicate a significant underlying change in an organization, or reflect the response of the network to an external event. Automatic detection of change points in evolving networks is rudimentary to the research and the understanding of the effect of such events on networks. Here we present an easy-to-implement and fast framework for change point detection in evolving temporal networks. Our method is size agnostic, and does not require either prior knowledge about the network’s size and structure, nor does it require obtaining historical information or nodal identities over time. We tested it over both synthetic data derived from dynamic models and two real datasets: Enron email exchange and AskUbuntu forum. Our framework succeeds with both precision and recall and outperforms previous solutions.
The performance of our framework with the KS distance metric for all permutations of ER networks that entail change. The networks were modeled with the edge connectivity probability,p1,ER,p2,ER∈ [0.05, 0.1, 0.15, …1],p1,ER≠p2,ER. Overall, the sensitivity graph depicts 380 experiments, in which each point is the average of 2x100 random networks. The framework excels in finding the hyper-parameter change. Hence, it is very sensitive to changes in the network’s density and fails to find a change only when the changes are very small, i.e., |p1,ER−p2,ER| = 0.05, as can be seen by the low f1 value at the narrow diagonal line.
Interesting framework! This size agnostic Change Point Detection sounds incredibly useful, especially with its ability to analyze evolving interaction networks without needing historical data. It almost feels like trying to predict when the T-Rex will pop up in the Dinosaur Game – you're looking for that shift, that change point, without knowing the future. A powerful tool for understanding temporal asynchronous human communication.
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This Size Agnostic Change Point Detection (CPD) framework is fascinating! Imagine applying it to Fnaf, tracking changes in animatronic activity patterns – a sudden spike in Freddy's movements could signal a new change point, like a shift in security guard strategy or even...something more sinister lurking in the shadows. The framework's adaptability is key, regardless of how big or small the changes are, it would be pretty suitable for identifying anomalies.
This Size Agnostic Change Point Detection (CPD) framework sounds incredibly useful! Imagine applying it to track trends in your Bitlife career. Did your happiness suddenly plummet after becoming a garbage collector? CPD could pinpoint that moment!
This CPD framework sounds fascinating! Detecting change points in network evolution without size restrictions is a real game-changer. It reminds me of navigating fresh powder in Snow Rider ; you need to react quickly to changes in the terrain. This approach could be incredibly useful for understanding communication patterns. Imagine using it to analyze shifts in user behavior after a new Snow Rider update!