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UID:c25a984e3971fc8d41d3459102c09e8a
CATEGORIES:Mathematical Physics Seminar
CREATED:20260722T134431
SUMMARY:Webinar: Cris Moore –  Which links matter most? Sparsifying network dynamics with effective resistance
LOCATION:Zoom 
DESCRIPTION:Cris Moore–  Santa Fe Institute\nWednesday, August 19, 2026\nZoom opens: 10
 :30AM EDT\nSeminar begins: 10:45AM EDT\nWhich links matter most? Sparsifyin
 g network dynamics with effective resistance\n“Sparsification” is the act o
 f reducing a network to a subset of its edges while approximately preservin
 g its properties: either to reduce the computational cost of solving proble
 ms about it, or to identify which edges are the most important in some sens
 e. Computer scientists have developed beautiful techniques for sparsifying 
 a graph using physics-related ideas like the effective resistance. However,
  while these methods preserve the spectral properties of the Laplacian, it 
 is not obvious to what extent they preserve the behavior of nonlinear dynam
 ical systems. Using a mobility network from the United States as an example
 , I’ll show that they do very well for the SIR epidemic model, including th
 e probability each node becomes infected and its distribution of arrival ti
 mes, even when the sparse network includes less than 10% of the original ed
 ges. Choosing edges using purely topological methods, or by thresholding ed
 ge weights, does not perform nearly as well. I will end by discussing the p
 ossibility of using sparsification to “denoise” networks from bioinformatic
 s, and present some preliminary results on the Kuramoto model of coupled os
 cillators.\nThis is joint work with Alexander Mercier (Harvard School of Pu
 blic Health), Emmie Fitz-Gibbons (Brown), and Sam Scarpino (Northeastern).\
 n
X-ALT-DESC;FMTTYPE=text/html:<p style="text-align: center;"><strong>Cris Moore– </strong><strong>&nbsp;S
 anta Fe Institute</strong></p><p style="text-align: center;"><strong>Wednes
 day,&nbsp;August 19,&nbsp;2026</strong></p><p style="text-align: center;"><
 strong>Zoom opens: 10:30AM EDT</strong></p><p style="text-align: center;"><
 strong>Seminar begins: 10:45AM EDT</strong></p><p style="text-align: center
 ;"><strong>Which links matter most? Sparsifying network dynamics with effec
 tive resistance</strong></p><p>“Sparsification” is the act of reducing a ne
 twork to a subset of its edges while approximately preserving its propertie
 s: either to reduce the computational cost of solving problems about it, or
  to identify which edges are the most important in some sense. Computer sci
 entists have developed beautiful techniques for sparsifying a graph using p
 hysics-related ideas like the effective resistance. However, while these me
 thods preserve the spectral properties of the Laplacian, it is not obvious 
 to what extent they preserve the behavior of nonlinear dynamical systems. U
 sing a mobility network from the United States as an example, I’ll show tha
 t they do very well for the SIR epidemic model, including the probability e
 ach node becomes infected and its distribution of arrival times, even when 
 the sparse network includes less than 10% of the original edges. Choosing e
 dges using purely topological methods, or by thresholding edge weights, doe
 s not perform nearly as well. I will end by discussing the possibility of u
 sing sparsification to “denoise” networks from bioinformatics, and present 
 some preliminary results on the Kuramoto model of coupled oscillators.</p><
 p>This is joint work with Alexander Mercier (Harvard School of Public Healt
 h), Emmie Fitz-Gibbons (Brown), and Sam Scarpino (Northeastern).</p>
DTSTAMP:20260828T011721
DTSTART;TZID=America/New_York:20260819T104500
DTEND;TZID=America/New_York:20260819T120000
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