RIT on Analysis of Complex Networks Archives for Fall 2016 to Spring 2017
Erdos-Renyi Random Graphs
When: Fri, February 12, 2016 - 2:00pmWhere: MATH1310
Speaker: Maria Cameron (UMCP, Math) - http://www.math.umd.edu/~mariakc/
Random Graphs
When: Fri, February 26, 2016 - 2:00pmWhere: MATH1310
Speaker: Maria Cameron (UMCP) - http://www.math.umd.edu/~mariakc/
Support of Laplacian eigenvectors on graphs
When: Fri, March 4, 2016 - 2:00pmWhere: MATH1310
Speaker: Matthew Begue (University of Maryland) -
Algebraic Analysis of Network Structure
When: Fri, March 25, 2016 - 2:00pmWhere: MATH1310
Speaker: Aaron Ostrander (UMCP, Physics) -
Abstract: Algebraic graph theory provides a natural language for formulating
problems on networks. We will consider various techniques for
detecting communities/modules, approximating optimal cut sets, and
testing whether a set of vertices is open.
Diffusion Maps and Diffusion Wavelets
When: Fri, April 1, 2016 - 3:00pmWhere: MATH 1310
Speaker: Kasso Okoudjou (UMCP, Math) - http://www.math.umd.edu/~okoudjou/
Diffusion Maps and Diffusion Wavelets
When: Fri, April 15, 2016 - 2:00pmWhere: MATH 1310
Speaker: Kasso Okoudjou (UMCP, Math) - http://www.math.umd.edu/~okoudjou/
Modeling the dynamics of gene networks
When: Fri, April 22, 2016 - 2:00pmWhere: MATH1310
Speaker: Michelle Girvan (UMCP, Physics) - http://www.networks.umd.edu
Compressed Sensing and Spin Glasses
When: Fri, April 29, 2016 - 2:00pmWhere: MATH 1310
Speaker: Siddharth Sharma (UMCP, Physics) - http://www.math.umd.edu/~mariakc/rit-analysis-of-complex.html
Abstract: Reconstruction of a signal from a limited number of measurements
is a crucial problem in complex networks. This is especially true in cases
where the observed behaviour is not completely explained by the mapped
structure e.g. Gene-regulatory networks. Compressed sensing has been a
major revolution in signal acquisition as it involves reconstructing a
signal with a number of measurements lesser than the actual length of the
signal. The technique relies on the signal being sparse in some basis so
only the necessary ³compressed² part is required for full reconstruction.
In this talk, I would discuss a probabilistic reconstruction which allows
compressed sensing to be performed at acquisition rates approaching the
theoretical optimal limits. The main idea would be to present the
inference problem as a spin-glass and to then use results from statistical
physics to reconstruct the signal through a belief propagation algorithm.
A Bayesian optimality analysis leading to the Nishimori conditions will
also be discussed.
Authority, Trust and Influence: The Complex Network of Social Media
When: Fri, May 6, 2016 - 2:00pmWhere: MATH 1310
Speaker: William Rand (UMCP, Robert H. Smith School of Business) - http://www.rhsmith.umd.edu/directory/william-rand
Abstract: The dramatic feature of social media is that it gives everyone a
voice; anyone can speak out and express their opinion to a crowd of
followers with little or no cost or effort, which creates a loud and
potentially overwhelming marketplace of ideas. Given this egalitarian
competition, how do users of social media identify authorities in this
crowded space? Who do they trust to provide them with the information
and the recommendations that they want? Which tastemakers have the
greatest influence on social media users? Using agent-based
modeling, machine learning and network analysis we begin to examine
and shed light on these questions and develop a deeper understanding
of the complex system of social media.