Where: MATH1310

Speaker: Maria Cameron (UMCP, Math) - http://www.math.umd.edu/~mariakc/

Where: MATH1310

Speaker: Maria Cameron (UMCP) - http://www.math.umd.edu/~mariakc/

Where: MATH1310

Speaker: Matthew Begue (University of Maryland) -

Where: 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.

Where: MATH 1310

Speaker: Kasso Okoudjou (UMCP, Math) - http://www.math.umd.edu/~okoudjou/

Where: MATH 1310

Speaker: Kasso Okoudjou (UMCP, Math) - http://www.math.umd.edu/~okoudjou/

Where: MATH1310

Speaker: Michelle Girvan (UMCP, Physics) - http://www.networks.umd.edu

Where: 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.

Where: 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.