Empower - The Campaign for Northeastern University


Network Science Institute: Network Reconstuction & Graph Distances: NetSI Collabathon Intermediate Progress Report

Title: Network Reconstruction & Graph Distances: NetSI Collabathon Intermediate Progress ReportSpeaker: Brennan Klein, Northeastern UniversityDate: Friday, March 29, 2019Time: 2:00pm - 3:00pmLocation: 11th Floor, 177 Huntington Ave, Boston MA, 02115AbstractAcross many disciplines, we analyze networks that have been reconstructed or inferred from time series data (e.g., changes in brain activity in neuroscience, shifting stock prices in economics, population dynamics in ecology). These networks can be reconstructed using a variety of techniques, but because different algorithms can output different networks, practitioners are often uncertain about whether their approach is suitable for describing the system in question. In January, NetSI hosted its first annual Collabathon where dozens of researchers worked for three days to start a collaboration and develop a software package, which has since grown to include 19 methods for reconstructing networks from time series data and 21 different network similarity / graph distance measures. Using this software, we compare different techniques for reconstructing networks from time series data. Instead of ranking these methods by their effectiveness at reconstructing networks, we cluster reconstruction techniques based on the similarity of the networks they output (using all 21 similarity / distance measures). The ultimate goal is to provide a map of the various tools that network scientists have at their disposal for studying networks generated from temporal data. This talk will be an update about the current state of the project, a description of the main and secondary results, and an opportunity for members of the NetSI community to weigh in with suggestions, comments, and critiques that will guide the closing stage of this project and improve the final output.About the SpeakerBrennan Klein studies how complex systems are able to represent, predict, and intervene on their surroundings across a number of different scales—all in ways that appear to avoid surprising states in the future. He uses this approach to study a range of phenomena from decision making, to experimental design, to causation and emergence in networks. His dissertation is An Apparent Teleology of Complex Networks, asking why there appears to be apparent purpose or goal-directedness to the structure, dynamics, and behavior of networks. Link to website: [www.brennanklein.com]

Friday, March 29 at 2:00pm



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