Node Alertness-Detecting changes in rapidly evolving graphs
arXiv:1907.11623v1
Originality Synthesis-oriented
AI Analysis
This work addresses change detection in dynamic graphs for financial applications, but appears incremental as it adapts local monitoring to a specific domain.
The authors tackled the problem of detecting changes in rapidly evolving large-scale graphs by introducing a notion of local alertness, where nodes monitor neighborhood changes, and applied it to cointegrated stock pairs in finance.
In this article we describe a new approach for detecting changes in rapidly evolving large-scale graphs. The key notion involved is local alertness: nodes monitor change within their neighborhoods at each time step. Here we propose a financial local alertness application for cointegrated stock pairs