Title:
The Geometry of Community Detection via the MMSE Matrix
The Geometry of Community Detection via the MMSE Matrix
Author(s)
Reeves, Galen
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Abstract
The information-theoretic limits of community detection have been studied extensively for network models with high levels of symmetry or homogeneity. In this talk, Reeves will present a new approach that applies to a broader class of network models that allow for variability in the sizes and behaviors of the different communities, and thus better reflect the behaviors observed in real-world networks. The results show that the ability to detect communities can be described succinctly in terms of a matrix of effective signal-to-noise ratios that provides a geometrical representation of the relationships between the different communities. This characterization follows from a matrix version of the I-MMSE relationship and generalizes the concept of an effective scalar signal-to-noise ratio introduced in previous work.
This work can be found online at https://arxiv.org/abs/1907.02496
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Date Issued
2019-09-04
Extent
39:39 minutes
Resource Type
Moving Image
Resource Subtype
Lecture