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Data mining in social network analysis
Zvirinský, Peter ; Mrázová, Iveta (advisor) ; Drotár, Peter (referee) ; Vidnerová, Petra (referee)
Title: Data mining in social network analysis Author: Mgr. Peter Zvirinský Department: Department of Theoretical Computer Science and Mathematical Logic Supervisor: doc. RNDr. Iveta Mrázová CSc., Department of Theoretical Com- puter Science and Mathematical Logic Abstract. In the past several years, the global economy has experienced a sig- nificant increase in overall debt, reaching 238% of the world GDP in 2022, as reported by the International Monetary Fund. This growing indebtedness raises concerns about the stability of the financial system and the welfare of individuals and institutions. It also underscores the need for e ective strategies to under- stand the intricate relationships between debtors and creditors and to mitigate associated risks. In response, this thesis proposes a novel approach based on data mining methods for the comprehensive analysis of debt formation patterns among individuals and companies, focusing on the largely untapped data from the Insolvency Register (IR) of the Czech Republic. We aim to leverage social network analysis (SNA) methods to model and analyze the interactions among subjects participating in insolvencies, namely debtors, creditors, and insolvency administrators. Additionally, we focus our research on dynamic social networks that capture structural changes in...
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Rigid body simulation
Zvirinský, Peter ; Pelikán, Josef (advisor) ; Kolomazník, Jan (referee)
The object of this work is to create an easily extendible rigid body simulation engine. The engine will contain real-time collision detection and rea- listic collision handling. It will be able to simulate common nature forces such as gravity, related different types of friction and resting forces. The simulation engine will mainly focus on spheres and their movement influenced by the forces mentioned before. The part of this work will be as well the examination of diffe- rent types of optimalization of such tasks as collision detection and use of parallel approach. 1
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Generative neural networks in image reconstruction
Honzátko, David ; Šorel, Michal (advisor) ; Zvirinský, Peter (referee)
Recent research in generative models came up with a promising approach to modelling the prior proba- bility of natural images. The architecture of these prior models is based on deep neural networks. Although these priors were primarily designed for generating new natural-like images, its potential use is much broader. One of the possible applications is to use these models for solving the inverse problems in low-level vision (i.e., image reconstruction). This usage is mainly possible because the architecture of these models allows computing the derivative of the prior probability with respect to the input image. The main objective of this thesis is to evaluate the usage of these prior models in image reconstruction. This thesis proposes a novel model-based optimization method to two image reconstruction problems - image denoising and single-image super-resolution (SISR). The proposed method uses optimization algorithms for finding the maximum-a- posteriori probability, which is defined using the above mentioned prior models. The experimental results demonstrate that the proposed approach achieves reconstruction performance competitive with the current state-of-the-art methods, especially regarding SISR.
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Social networks and data mining
Zvirinský, Peter ; Mrázová, Iveta (advisor) ; Neruda, Roman (referee)
Recent data mining methods represent modern approaches capable of analyzing large amounts of data and extracting meaningful and potentially useful information from it. In this work, we discuss all the essential steps of the data mining process - including data preparation, storage, cleaning, data analysis as well as visualization of the obtained results. In particular, this work is focused on the data available publicly from the Insolvency Register of the Czech Republic, that comprises all insolvency proceedings commenced after 1. January 2008 in the Czech Republic. With regard to the considered type of data, several data mining methods have been discussed, implemented, tested and evaluated. Among others, the studied techniques include Market Basket Analysis, Bayesian networks and social network analysis. The obtained results reveal several social patterns common in the current Czech society.
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Rigid body simulation
Zvirinský, Peter ; Pelikán, Josef (advisor) ; Kolomazník, Jan (referee)
The object of this work is to create an easily extendible rigid body simulation engine. The engine will contain real-time collision detection and rea- listic collision handling. It will be able to simulate common nature forces such as gravity, related different types of friction and resting forces. The simulation engine will mainly focus on spheres and their movement influenced by the forces mentioned before. The part of this work will be as well the examination of diffe- rent types of optimalization of such tasks as collision detection and use of parallel approach. 1
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Strigil: A framework for data extraction
Zvirinský, Peter
Data scraping is a way to gather and integrate data from different data sources. In this presentation, we will describe Strigil, a framework for automatized screen-scraping. It allows to define custom scraping scripts in intuitive graphical user interface and provides a solution for scalable and distributed scraping.
Slides: idr-497_1 -  PDF
Video: idr-497_2 -  MP4
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