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DESCRIPTION:Community Detection in Networks: Pruning and Picking Parameters
 \n\nPeter J. Mucha\, Dartmouth College\n	Tuesday April 22\, 12-1pm\n	Zoom Li
 nk: https://mcgill.zoom.us/j/89914150820\n	In Person: 550 Sherbrooke\, Room
  189\n	\n	Abstract: Real-world networks are neither completely random nor fu
 lly regular\, frequently containing essential structural features whose id
 entification can help better understand the nature and purpose of a networ
 k. One common task is to seek out clusters in the data\, sometimes describ
 ed as 'community detection'. Numerous software packages are available and 
 widely used for community detection\, but many of these require parameters
  to be selected (or assume default values) that are not always obvious to 
 application domain experts. For example\, the best use of modularity-based
  methods includes setting a parameter to control the resolution. Moreover\
 , most of the algorithms are pseudo-random heuristic approximations. As su
 ch\, one frequently needs to reconcile numerous different partitions of no
 des into communities while simultaneously exploring the parameter space. T
 hese problems are exacerbated when community detection is extended to mult
 ilayer networks\, because of the addition of at least one parameter to spe
 cify the coupling between layers. To address these difficulties\, we combi
 ne different theoretical and computational developments into a simple fram
 ework for pruning a set of partitions to a subset that are self-consistent
  by an equivalence with stochastic block model (SBM) inference. Implementi
 ng these pruning steps together typically highlights only a small number o
 f 'stable' (fixed point) partitions\, making it easier for users to focus 
 their attention on a smaller number of partitions. Our framework works for
  single networks and multilayer networks\, as well as for restricting to a
  fixed number of communities when desired. Our intention is to make it rel
 atively easy for application domain experts to use these methods\, with co
 de for implementing these procedures available at http://github.com/ragibs
 on/ModularityPruning.\n\nBiographical Sketch: Peter Mucha is the Jack Byrn
 e Distinguished Professor in Mathematics at Dartmouth College. Born in Tex
 as and raised in Minnesota\, Mucha attended college at Cornell University 
 where he majored in Engineering Physics. After a Churchill Scholarship stu
 dying in the Cavendish Laboratory at Cambridge with an M.Phil. in Physics\
 , he returned to the States to study Applied and Computational Mathematics
  at Princeton\, earning M.A. and Ph.D. degrees. Following a postdoctoral i
 nstructorship in applied mathematics at MIT and assistant professorship in
  Mathematics at Georgia Tech\, he moved to UNC-Chapel Hill for 16 years\, 
 where he served as chair of the Department of Mathematics\, the founding c
 hair of the Department of Applied Physical Sciences\, and the Director of 
 the Chairs Leadership Program at the Institute for the Arts & Humanities. 
 His awards include a DOE Early Career PI award\, an NSF CAREER award\, and
  recognition as an HHMI Gilliam Advisor. Mucha arrived at Dartmouth in 202
 1 as part of The Jack Byrne Academic Cluster in Mathematics and Decision S
 cience.\n
DTSTART:20250422T160000Z
DTEND:20250422T170000Z
SUMMARY:QLS Seminar Series -  Peter J. Mucha
URL:/qls/channels/event/qls-seminar-series-peter-j-muc
 ha-363994
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