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DESCRIPTION:Community Detection in Networks: Pruning and Picking Parameters
 \n\nPeter J. Mucha\, Dartmouth College\n	Tuesday November 11\, 12-1pm\n	Zoom
  Link: https://mcgill.zoom.us/j/87078928687\n	In Person: 550 Sherbrooke\, R
 oom 189\n	\n	Abstract: Real-world networks are neither completely random nor
  fully regular\, frequently containing essential structural features whose
  identification can help better understand the nature and purpose of a net
 work. One common task is to seek out clusters in the data\, sometimes desc
 ribed as 'community detection'. Numerous software packages are available a
 nd widely used for community detection\, but many of these require paramet
 ers to be selected (or assume default values) that are not always obvious 
 to application domain experts. For example\, the best use of modularity-ba
 sed methods includes setting a parameter to control the resolution. Moreov
 er\, most of the algorithms are pseudo-random heuristic approximations. As
  such\, one frequently needs to reconcile numerous different partitions of
  nodes into communities while simultaneously exploring the parameter space
 . These problems are exacerbated when community detection is extended to m
 ultilayer networks\, because of the addition of at least one parameter to 
 specify the coupling between layers. To address these difficulties\, we co
 mbine different theoretical and computational developments into a simple f
 ramework for pruning a set of partitions to a subset that are self-consist
 ent by an equivalence with stochastic block model (SBM) inference. Impleme
 nting these pruning steps together typically highlights only a small numbe
 r of 'stable' (fixed point) partitions\, making it easier for users to foc
 us their attention on a smaller number of partitions. Our framework works 
 for single networks and multilayer networks\, as well as for restricting t
 o a fixed number of communities when desired. Our intention is to make it 
 relatively easy for application domain experts to use these methods\, with
  codes available in both Python (modularity pruning) and R (ideanet).\n\nB
 iographical Sketch: Peter Mucha is the Jack Byrne Distinguished Professor 
 in Mathematics at Dartmouth College. Born in Texas and raised in Minnesota
 \, Mucha attended college at Cornell University where he majored in Engine
 ering Physics. After a Churchill Scholarship studying in the Cavendish Lab
 oratory at Cambridge with an M.Phil. in Physics\, he returned to the State
 s to study Applied and Computational Mathematics at Princeton\, earning M.
 A. and Ph.D. degrees. Following a postdoctoral instructorship in applied m
 athematics at MIT and assistant professorship in Mathematics at Georgia Te
 ch\, he moved to UNC-Chapel Hill\, where he served as chair of the Departm
 ent of Mathematics\, the founding chair of the Department of Applied Physi
 cal Sciences\, and the Director of the Chairs Leadership Program at the In
 stitute for the Arts & Humanities. His awards include a DOE Early Career P
 I award\, an NSF CAREER award\, and recognition as an HHMI Gilliam Advisor
 . Mucha moved to Dartmouth in 2021 as part of The Jack Byrne Academic Clus
 ter in Mathematics and Decision Science.\n
DTSTART:20251111T170000Z
DTEND:20251111T180000Z
SUMMARY:QLS Seminar Series - Peter J. Mucha
URL:/centre-montreal/channels/event/qls-seminar-series
 -peter-j-mucha-368662
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