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SUMMARY:Applied math. talk: Heatmap centrality: a new measure to identify super-spreader nodes in scale-free networks by Christina Duron\, the University of Arizona
DESCRIPTION:Abstract: The identification of potential super-spreader nodes within a network is a critical part of the study and analysis of real-world networks. Motivated by a new interpretation of the “shortest path” between two nodes\, this talk will explore the properties of the recently proposed measure\, the heatmap centrality\, by comparing the farness of a node with the average sum of farness of its adjacent nodes in order to identify influential nodes within the network. As many real-world networks are often claimed to be scale-free\, numerical experiments based upon both simulated and real-world undirected and unweighted scale-free networks are used to illustrate the effectiveness of the new “shortest path” based measure with regards to its CPU run time and ranking of influential nodes.
URL:https://colleges.claremont.edu/ccms/event/applied-math-talk-heatmap-centrality-a-new-measure-to-identify-super-spreader-nodes-in-scale-free-networks-by-christina-duron-the-university-of-arizona/
LOCATION:Zoom meeting\, United States
CATEGORIES:Applied Math Seminar
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DTSTART;TZID=America/Los_Angeles:20210224T161500
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SUMMARY:Prof. Lori Ziegelmeier
DESCRIPTION:Title:  Using Topology to Measure Shape in Data \nAbstract: Data of various kinds is being collected at an enormous rate\, and in many different forms. Often\, the data are equipped with a notion of distance that reflects similarity in some sense. Using this similarity measure\, certain topological features–e.g. the number of connected components\, loops\, and trapped volumes–can be ascertained and can provide insight into the structure of these complex data sets. In this talk\, I will introduce topology and a fundamental tool of topological data analysis\, persistent homology. Then\, we will see how these tools can be used for clustering\, with machine learning\, and to explain features in data. In particular\, we will discuss (1) using persistence to explore the relationship between country development and geography\, (2) vectorizing persistence information via a persistence image to analyze the discrete dynamical system of the linked twist map\, and (3) explore notions of minimal generators to extract geometric meaning from homological features. \nDr. Ziegelmeier is an Associate Professor at Macalester College.
URL:https://colleges.claremont.edu/ccms/event/prof-lori-ziegelmeier/
LOCATION:Zoom
CATEGORIES:Colloquium
ORGANIZER;CN="Andrew Bernoff":MAILTO:ajb@hmc.edu
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