Title:
The Geometry of Community Detection via the MMSE Matrix
The Geometry of Community Detection via the MMSE Matrix
| dc.contributor.author | Reeves, Galen | |
| dc.contributor.corporatename | Georgia Institute of Technology. Machine Learning | en_US |
| dc.contributor.corporatename | Duke University. Dept. of Electrical and Computer Engineering | en_US |
| dc.contributor.corporatename | Duke University. Dept. of Statistical Science | en_US |
| dc.date.accessioned | 2019-09-09T18:53:48Z | |
| dc.date.available | 2019-09-09T18:53:48Z | |
| dc.date.issued | 2019-09-04 | |
| dc.description | Presented on September 4, 2019 at 12:15 p.m. in the Marcus Nanotechnology Building, Room 1116. | en_US |
| dc.description | Galen Reeves joined the faculty at Duke University in Fall 2013, and is currently an Assistant Professor with a joint appointment in the Department of Electrical & Computer Engineering and the Department of Statistical Science. | en_US |
| dc.description | Runtime: 39:39 minutes | en_US |
| dc.description.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 | en_US |
| dc.format.extent | 39:39 minutes | |
| dc.identifier.uri | http://hdl.handle.net/1853/61835 | |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartofseries | Machine Learning @ Georgia Tech (ML@GT) Seminar Series | |
| dc.subject | Community detection | en_US |
| dc.subject | Information theory | en_US |
| dc.subject | Machine learning | en_US |
| dc.title | The Geometry of Community Detection via the MMSE Matrix | en_US |
| dc.type | Moving Image | |
| dc.type.genre | Lecture | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | Machine Learning Center | |
| local.contributor.corporatename | College of Computing | |
| local.relation.ispartofseries | ML@GT Seminar Series | |
| relation.isOrgUnitOfPublication | 46450b94-7ae8-4849-a910-5ae38611c691 | |
| relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
| relation.isSeriesOfPublication | 9fb2e77c-08ff-46d7-b903-747cf7406244 |
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