András Bóta

András Bóta
Luleå University of Technology | LTU · Department of Computer Science, Electrical and Space Engineering (SRT)

PhD

About

27
Publications
4,892
Reads
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239
Citations
Citations since 2016
16 Research Items
198 Citations
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2016201720182019202020212022010203040506070
2016201720182019202020212022010203040506070
Introduction
My main research interests lie in the fields of complex network analysis and artificial intelligence. While I’m interested in both theoretical and applied research, I have a strong preference towards the latter. I’ve worked on applied projects in the fields of economics, linguistics and biology, but most of my recent work involves medical applications, specifically risk analysis in epidemic modelling.
Additional affiliations
September 2018 - present
Umeå University
Position
  • PostDoc Position
August 2015 - August 2017
UNSW Sydney
Position
  • Research Associate
October 2009 - May 2015
University of Szeged
Position
  • PhD Student
Education
September 2007 - June 2009
University of Debrecen
Field of study
  • Computer Science
September 2003 - February 2007
University of Pécs
Field of study
  • Computer Science

Publications

Publications (27)
Article
Full-text available
Brands can be defined as psychological constructs residing in our minds. By analyzing brand associations, we can study the mental constructs around them. In this paper, we study brands as parts of an associative network based on a word association database. We explore the communities–closely-knit groups in the mind–around brand names in this struct...
Article
Full-text available
Pharmacovigilance is a science that involves the ongoing monitoring of adverse drug reactions to existing medicines. Traditional approaches in this field can be expensive and time-consuming. The application of natural language processing (NLP) to analyze user-generated content is hypothesized as an effective supplemental source of evidence. In this...
Article
Full-text available
Both community detection and influence maximization are well-researched fields of network science. Here, we investigate how several popular community detection algorithms can be used as part of a heuristic approach to influence maximization. The heuristic is based on the community value, a node-based metric defined on the outputs of overlapping com...
Article
Full-text available
Identifying the critical factors related to influenza spreading is crucial in predicting and mitigating epidemics. Specifically, uncovering the relationship between epidemic onset and various risk indicators such as socioeconomic, mobility and climate factors can reveal locations and travel patterns that play critical roles in furthering an outbrea...
Article
Full-text available
The mental lexicon stores words and information about words. The lexicon is seen by many researchers as a network, where lexical units are nodes and the different links between the units are connections. Based on the analysis of a word association network, in this article we show that different kinds of associative connections exist in the mental l...
Preprint
Full-text available
The first influenza pandemic in our century started in 2009, spreading from Mexico to the rest of the world, infecting a noticeable fraction of the world population. The outbreak reached Europe in late April, and eventually, almost all countries had confirmed H1N1 cases. On 6 May, Swedish authorities reported the first confirmed influenza case. By...
Article
Full-text available
As more and more cities adopt the use of smart cards as means to access public transportation networks, it becomes much easier to discover the mobility patterns of individual passengers. A natural way to represent such patters is in the form of graphs. In this paper we analyze the public transportation network of a major metropolitan area from a un...
Conference Paper
Full-text available
Abstract—Community detection is a widely discussed topicin network science which allows us to discover detailed in-formation about the connections between members of a givengroup. Communities play a critical role in the spreading ofviruses or the diffusion of information. In [1], [8] Kempe et al.proposed the Independent Cascade Model, defining a si...
Article
Full-text available
Background An unprecedented Zika virus epidemic occurred in the Americas during 2015-2016. The size of the epidemic in conjunction with newly recognized health risks associated with the virus attracted significant attention across the research community. Our study complements several recent studies which have mapped epidemiological elements of Zika...
Data
The excel file includes the following input data and model results a) Regional population density, GDP and monthly reported case data used as input in the model; b) Monthly Case data for each country; c) The top 100 routes likely to result in local Zika transmission at the destination and their time-dependent relative risk estimates; d) The complet...
Article
Full-text available
Modern public transport networks provide an efficient medium for the spread of infectious diseases within a region. The ability to identify components of the public transit system most likely to be carrying infected individuals during an outbreak is critical for public health authorities to be able to plan for outbreaks, and control their spread. I...
Article
Full-text available
Background: Obesity is a global epidemic associated with aging-like cellular processes. Resistance to hormones such as leptin and insulin are hallmarks in obesity. Contributing factors in obesity and overweight, including: lifestyle habits, hypothyroidism and genetics, can cause changes in the levels of metabolic parameters. Exercising is known to...
Article
Full-text available
Modeling the spread of infections on networks is a well-studied and important field of research. Most infection and diffusion models require a real value or probability on the edges of the network as an input, but this is rarely available in real-life applications. Our goal in this paper is to develop a general framework for this task. The general...
Conference Paper
Identifying components of the public transit system most likely to exacerbate disease spread is critical for public health authorities to be able to plan for epidemics and control their spread. In this work we propose a method to detect such components in a transit network using a three-stage approach. We first use results from a transit simulation...
Chapter
Full-text available
Several methods have been proposed recently to estimate the edge infection probabilities in infection or diffusion models. In this paper we will use the framework of the Generalized Cascade Model to define the Inverse Infection Problem—the problem of calculating these probabilities. We are going to show that the problem can be reduced to an optimiz...
Conference Paper
Full-text available
In this paper we present preliminary results for a fast parallel adaptation of the well-known k-means clustering algorithm to graphs. We are going to use our method to detect communities in complex networks. For testing purposes we will use the graph generator of Lancichinetti et al., and we are going to compare our method with the OSLOM, CPM, and...
Thesis
Full-text available
The roots of graph theory lead back to the puzzle of Königsberg’s bridges. In 1736 Leonhardt Euler published a paper on this problem, and also proposed a solution for it. Since then much has been learned about the mathematical properties of graphs, and the field has a long history of applications including sociology, biology or operations research...
Article
Full-text available
In this paper we propose a clique-based high-resolution overlapping community detection algorithm. The hub percolation method is able to find a large number of highly overlapping communities. Using different hub-selection strategies and parametrization we are able to fine tune the resolution of the algorithm. We also propose a weighted hub-selectio...
Article
Full-text available
The Domingos-Richardson model, along with several other infection models, has a wide range of applications in prediction. In most of these, a fundamental problem arises: the edge infection probabilities are not known. To provide a systematic method for the estimation of these probabilities, the authors have published the Generalized Cascade Model a...
Conference Paper
Full-text available
The applications of infection models like the Linear Threshold or the Domingos-Richardson model requires a graph weighted with infection probabilities. In many real-life appli-cations these probabilities are unknown; therefore a systematic method for the estimation of these probabilities is required. One of the methods proposed to solve this proble...
Conference Paper
Full-text available
One of the defining characteristics of small-world networks is that their edge distribution is globally and locally inhomogeneous: nodes form dense groups inside the networks. These groups are called communities. In this paper we will use the hub percolation community detection method of Bóta et al. to examine and compare the community structure of...
Article
Full-text available
The study of infection processes is an important field of science both from the theoretical and the practical point of view, and has many applications. In this paper we focus on the popular Independent Cascade model and its generalization. Unfortunately the exact computation of infection probabilities is a #P-complete problem [Chen2010], so one can...
Article
Full-text available
Overlapping community detection has already become an interesting prob-lem in data mining and also a useful technique in applications. This underlines the importance of following the lifetime of communities in real graphs. Palla et al. developed a promising method, and analyzed community evolution on two large databases [23]. We have followed their...
Article
Full-text available
In this paper we propose a method for estimating the edge infection probabilities in a generalized Domingos-Richardson model. The probabilities are considered as unknown functions of a priori known edge attributes. To handle this inverse infection problem, we divide the past data to learning and test sets. Then we try to assign edge probabilities s...
Conference Paper
Full-text available
We survey and unify the methods developed for finding over-lapping communities in Small World graphs in the recent years. The results have impact on graph mining; we give some demonstration of this.
Conference Paper
Full-text available
In this paper we propose a simple propagation algorithm for protein classification. It is similar to existing label propagation algorithms, with some important differences. The method is general so it can be used for other purposes as well, although in this paper it is used solely for solving a binary classification problem, and its results are eva...

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Projects

Projects (6)
Archived project
The purpose of this study is to review the existing evidence on, and the effectiveness of natural language processing to understand user-generated content for the purpose of pharmacovigilance.
Project
The objective of this study is to conduct a systematic review of the literature that used textual content published to social media, for the purpose of the surveillance and prediction of communicable diseases.
Project
The project involves modelling the risk factors behind seasonal influenza outbreaks in Sweden with an emphasis on predicting parameters of future outbreaks. The project includes modelling the 2009-2010 Swine flu outbreak and trying to understand why this outbreak was different from the usual seasonal influenza.