Learning Large-Scale Patterns in Complex Networks
Assistant Professor, Department of Computer Science, and the BioFrontiers Institute
University of Colorado, Boulder, and External Faculty, Santa Fe Institute
Tuesday, September 16, 2014|
4:00pm - 5:30pm
Add to Google Calendar
About the Event
Networks provide a rich and mathematically principled approach to characterizing the structure of complex social and biological systems. A common step in understanding the structure and function of real-world networks is to characterize their large-scale organizational pattern via community detection, in which we aim to find a network partition that groups together vertices with similar connectivity patterns. Modern networks, however, often include rich auxiliary information, in the form of edge weights, vertex attributes, multi-partite structures, and edges that vary over time, and we often wish to incorporate these details into the network analysis.
Aaron Clauset is an Assistant Professor in the Department of Computer Science and the BioFrontiers Institute at the University of Colorado Boulder, and is External Faculty at the Santa Fe Institute. He received a PhD in Computer Science, with distinction, from the University of New Mexico, a BS in Physics, with honors, from Haverford College, and was an Omidyar Fellow at the prestigious Santa Fe Institute.
Open to: Public