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Activity Number:
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1
- Invited E-Poster Session
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Type:
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Invited
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Date/Time:
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Sunday, August 2, 2020 : 12:30 PM to 3:30 PM
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Sponsor:
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Section on Statistics in Imaging
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Abstract #313205
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Title:
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A Random Effects Stochastic Block Model for Joint Community Detection in Multiple Networks with Applications to Neuroimaging
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Author(s):
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Yuguo Chen*
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Companies:
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University of Illinois at Urbana-Champaign
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Keywords:
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Community detection;
Neuroimaging;
Non-negative matrix factorization;
Population of networks;
Random effects stochastic block model
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Abstract:
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To analyze data from multi-subject experiments in neuroimaging studies, we develop a modeling framework for joint community detection in a group of related networks, which can be considered as a sample from a population of networks. The proposed random effects stochastic block model facilitates the study of group differences and subject-specific variations in the community structure. We also develop a resampling-based hypothesis test for differences in community structure in two populations both at the whole network level and node level. The methodology is applied to a publicly available fMRI dataset from multi-subject experiments involving schizophrenia patients.
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Authors who are presenting talks have a * after their name.
