Events
Department of Mathematics and Statistics
Texas Tech University
Network data, characterized by interconnected nodes and edges, is pervasive in various domains and has gained significant popularity in recent years. In network data analysis, testing the presence of community structure in a network is one of the most important research tasks. Existing tests are mainly developed for unweighted networks. In practice, many real networks are weighted and our simulation study shows that the existing methods designed for unweighted networks may not be powerful for testing weighted networks. In this paper, we study the problem of testing the existence of a community structure in general networks that are either unweighted or weighted, and either dense or sparse. Firstly, we derive an information-theoretical limit for the existence of consistent tests. Based on the limits, we provide a quantification of the information loss in converting weighted networks to binary networks. Then we propose two new tests, namely, the weighted signed-triangle test and the empirical likelihood test. We find that both methods outperform the existing tests when the network size is small; the empirical likelihood test may further outperform the weighted signed-triangle test in small networks.
Please virtually attend this week's Statistics seminar at 4:00 PM (UT-5) via this zoom link.
Meeting ID: 966 2813 5072
Passcode: 358713
 | Tuesday
Apr. 9
5 PM
MA 109
|
| Real-Algebraic Geometry
Hessian II
David Weinberg
Mathematics and Statistics, Texas Tech University
|
Abstract. In decision-making applications where multiple forward simulations are needed, such as parameter study, design optimization, optimal control, uncertainty quantification, and inverse problems, we need to repeatedly solve forward problems. However, subject to the model complexity and the fineness of the discretization, the computational cost of forward simulations can be high. It may take a long time to complete a single forward simulation with the available computing resource. In this talk, we will introduce various reduced order modeling techniques, which aim to lower the computational complexity and maintain a good accuracy, including projection-based intrusive nonlinear model reduction and non-intrusive model reduction approaches. We will demonstrate the implementation of these reduced order modeling techniques in libROM (www.librom.net) and its application to numerical solvers for solving various physics problems.
When: 4:00 pm (Lubbock's local time is GMT -5)
Where: room MATH 011 (basement)
ZOOM details:
- Choice #1: use this link
Direct Link that embeds meeting and ID and passcode.
- Choice #2: join meeting using this link
Join Meeting, then you will have to input the ID and Passcode by hand:
* Meeting ID: 944 4492 2197
* Passcode: applied
 | Thursday
Apr. 11
6:30 PM
MA 108
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| Mathematics Education
Math Circle
Álvaro Pámpano
Department of Mathematics and Statistics, Texas Tech University
|
Math Circle spring poster
abstract 2 PM CDT (UT-5)