Cris Moore– S anta Fe Institute
Wednes day, August 19, 2026
< strong>Zoom opens: 10:30AM EDT
< strong>Seminar begins: 10:45AM EDT
Which links matter most? Sparsifying network dynamics with effec tive resistance
“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>This is joint work with Alexander Mercier (Harvard School of Public Healt h), Emmie Fitz-Gibbons (Brown), and Sam Scarpino (Northeastern). DTSTAMP:20260828T152751 DTSTART;TZID=America/New_York:20260819T104500 DTEND;TZID=America/New_York:20260819T120000 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR