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How are ownership relationships distributed in the geographical space? Is physical proximity a significant factor in investment decisions? What is the impact of the capital city? How can the structure of investment patterns characterize the attractiveness and development of economic regions? To explore these issues, we analyze the network of company ownership in Hungary and determine how are connections are distributed in geographical space. Based on the calculation of the internal and external linking probabilities, we propose several measures to evaluate the attractiveness of towns and geographic regions. Community detection based on several null models indicates that modules of the network coincide with administrative regions, in which Budapest is the absolute centre, and where county centres function as hubs. Gravity model-based modularity analysis highlights that, besides the strong attraction of Budapest, geographical distance has a significant influence over the frequency of connections and the target nodes play the most significant role in link formation, which confirms that the analysis of the directed company-ownership network gives a good indication of regional attractiveness.

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This paper investigates the role of socioeconomic considerations in the formation of official COVID-19 reports. To this end, we employ a dataset that contains 1159 pre-processed indicators from the World Bank Group GovData360 and TCdata360 platforms and an additional 8 COVID-19 variables generated based on reports from 138 countries. During the analysis, a rank-correlation-based complex method is used to identify the time- and space-varying relations between pandemic variables and the main topics of World Bank Group platforms. The results not only draw attention to the importance of factors such as air traffic, tourism, and corruption in report formation but also support further discipline-specific research by mapping and monitoring a wide range of such relationships. To this end, a source code written in R language is attached that allows for the customization of the analysis and provides up-to-date results.

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There are several well-known rankings of universities and higher education systems. Numerous recent studies question whether it is possible to compare universities and countries of different constitutions. These criticisms stand on solid ground. It is impossible to create a onedimensional ordering that faithfully compares complex systems such as universities or even higher education systems. We would like to convince the reader that using well-chosen elements of a family of state-of-the-art data mining methods, namely, bi-clustering methods, can provide an informative picture of the relative positions of universities/higher education systems. Bi-clustering methods produce leagues of comparable entities alongside the indicators, which produce a similar grouping of them. Within leagues, partial rankings could be specified and furthermore can serve as a proper basis for benchmarking.Tertiary Education and Management (2019) 25:289-310 comparison of universities or countries with very different financial backgrounds, scope and social environments. There is an ongoing effort (Downing 2013;Salmi 2013) to define different and well-tailored leagues for benchmarking universities or countries instead of ranking them in a single group. However, there is no generally accepted method for identifying such leagues.The authors agree with Benneworth (2010) and Liu (2013) that universities that belong to similar higher education systems should be compared according to a given set of criteria that is also in accordance with the common features of the higher education systems. The present work hinges on a fundamental principle: leagues (of countries) constitute both the set of countries and the set of indicators. Specifically, the set of criteria might differ from league to league; however, some of the criteria may be common. This may also apply to universities. The primary challenge in specifying leagues is to simultaneously define a set of criteria and countries/universities that are similar according to the criteria.The purpose of this study is to answer the following research question: How can leagues of comparable higher education systems be defined? The study does not aim at expanding the criticisms of the indicators of rankings, nor replacing them with other (better) indicators, nor developing new indicators.In the following, for the case of higher education systems, we show how leagues, as a new basis for comparing higher education systems, can be developed. We use the available indicators as they are, acknowledging that some of them may impose some type of bias, while they are the result of enormous efforts of data acquisition and cleansing, which we surely cannot reproduce. In addition, the usage of well-known indicators follows the ceteris paribus principle; we introduce a new method forming groups of objects to compare, but we do not introduce new indicators at the same time. As a result, the gain of forming leagues can be demonstrated without the effect of new indicators.In this paper, we focus on creating leagues of ...