Network topology change-point detection from graph signals with prior spectral signatures

10/21/2020
by   Chiraag Kaushik, et al.
0

We consider the problem of sequential graph topology change-point detection from graph signals. We assume that signals on the nodes of the graph are regularized by the underlying graph structure via a graph filtering model, which we then leverage to distill the graph topology change-point detection problem to a subspace detection problem. We demonstrate how prior information on the spectral signature of the post-change graph can be incorporated to implicitly denoise the observed sequential data, thus leading to a natural CUSUM-based algorithm for change-point detection. Numerical experiments illustrate the performance of our proposed approach, particularly underscoring the benefits of (potentially noisy) prior information.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/11/2017

Inductive Conformal Martingales for Change-Point Detection

We consider the problem of quickest change-point detection in data strea...
research
10/15/2019

Distributed Change Detection in Streaming Graph Signals

Detecting abrupt changes in streaming graph signals is relevant in a var...
research
06/18/2020

Offline detection of change-points in the mean for stationary graph signals

This paper addresses the problem of segmenting a stream of graph signals...
research
03/06/2013

Large-Margin Metric Learning for Partitioning Problems

In this paper, we consider unsupervised partitioning problems, such as c...
research
10/26/2022

Optimal Sub-sampling to Boost Power of Kernel Sequential Change-point Detection

We present a novel scheme to boost detection power for kernel maximum me...
research
06/01/2023

Optimal Sequential Detection by Sparsity Likelihood

Consider the problem on sequential change-point detection on multiple da...
research
06/28/2018

First-order optimal sequential subspace change-point detection

We consider the sequential change-point detection problem of detecting c...

Please sign up or login with your details

Forgot password? Click here to reset