Activity Number:
|
93
- Recent Advances in Statistical Inference on Network Data
|
Type:
|
Invited
|
Date/Time:
|
Monday, August 8, 2022 : 8:30 AM to 10:20 AM
|
Sponsor:
|
IMS
|
Abstract #320661
|
|
Title:
|
Efficient Local Change-Point Detection for Complex Networks with Applications
|
Author(s):
|
Shirshendu Chatterjee* and Sharmodeep Bhattacharyya
|
Companies:
|
City University of New York, City College and Oregon State University
|
Keywords:
|
network inference;
change-point;
detection and localization;
neuroscience;
complex networks
|
Abstract:
|
We consider the problem of local change point detection in a sequence of network data. Most of the available methods for change-point detection in the context of networks are tailored for global change-point estimation. We will present new statistical methods and theories as well as innovative computationally efficient and provably consistent algorithms for interpretable change-point detection with real-world use cases from the domains of neuroscience and climate science. Specifically, we will focus on estimation of local change-points in network data sets as well as detection of local community structures in vertex-neighborhoods of network data sets. We will also demonstrate the performance of our methods in comparison with other relevant using simulation.
|
Authors who are presenting talks have a * after their name.