# Node Alertness-Detecting changes in rapidly evolving graphs

## TL;DR

This paper introduces a novel method for detecting changes in large, rapidly evolving graphs by leveraging local alertness at nodes, with an application to financial stock pairs.

## Contribution

It proposes a new local alertness-based approach for change detection in dynamic graphs, specifically applied to financial data.

## Key findings

- Effective detection of changes in large-scale dynamic graphs
- Application demonstrated on cointegrated stock pairs
- Potential for real-time monitoring in financial networks

## Abstract

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