Publishing December! CSCS Faculty Marisa Eisenberg and CSCS Fellow Jean-Gabriel Young add some prestigious journals to their resume of publications
A productive publishing month continues for the Complex Systems group.
Marisa Eisenberg, along with Adriana Dawes, and Padmanabhan Seshaiyer were awarded a grant from the NSF titled Collaborative Research: RoL: FELS: Workshop – Rules of Life in the Context of Future Mathematical Sciences . The three were charged with identifying opportunities for one of the NSF's 10 Big Ideas in mathematical sciences. Their particular big idea: Understanding the Rules of Life - defined on the NSF website as "Elucidating the sets of rules that predict an organism's observable characteristics, its phenotype."
The group solicited feedback from scholars across the range of this topic and came up with six key topic areas which you can read in their SIAM NEWS publication.
From the paper:
The intellectual impact of supporting research in these emerging priority areas is clear. Advances at the interface of mathematics and life sciences urgently need rigorous and comprehensive quantitative methods. In addition to furthering our understanding of the rules of life, these efforts will grow convergent research and make the most of the data revolution.
Jean-Gabriel Young with collaborators Guillaume St-Onge, Edward Laurence, Charles Murphy, Laurent Hébert-Dufresne, and Patrick Desrosiers, after a long collaboration, publish "Phase Transition in the Recoverability of Network History" Phys. Rev. X 9, 041056 – Published 17 December 2019.
As tweeted by Laurent Hébert-Dufresne on December 17 - "Temporal reconstruction of this project: Started at @DynamicaLab and @sfiscience, finished @UMICHCS and @uvmcomplexity. Good science requires great friends. If possible, travel to hang out with friends wherever they are."
Author article summary:
Networks really are dynamical objects: they have a past, present, and future. However, when we study real networks, we often only have access to a few snapshots taken at specific points in time, instead of a complete historical record. And without a precise record, some predictions are much harder to make. In our work, we ask whether we can use our partial observations to reconstruct the compete history of networks. We argue that we often can, and we show how to do it. But we also identify fundamental limitations to reconstruction.
Network zoo. Examples of networks generated by the process with b=1 (top row) and b=0.75 (bottom row), and γ∈{−10,−1,0,1,10} (from left to right). The width and color of edges encode this history; older edges are drawn with thick, dark strokes, while younger edges are drawn using thin, light strokes. The age of the nodes is encoded in their radius.
