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Activity Number:
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93
- Recent Advances in Statistical Inference on Network Data
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Type:
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Invited
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Date/Time:
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Monday, August 8, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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Abstract #320661
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Title:
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Efficient Local Change-Point Detection for Complex Networks with Applications
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Author(s):
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Shirshendu Chatterjee* and Sharmodeep Bhattacharyya
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Companies:
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City University of New York, City College and Oregon State University
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Keywords:
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network inference;
change-point;
detection and localization;
neuroscience;
complex networks
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Abstract:
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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.
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Authors who are presenting talks have a * after their name.
