Diversity and Polarization of Research Performance: Evidence from Hungary

Measuring the intellectual diversity encoded in publication records as a proxy to the degree of interdisciplinarity has recently received considerable attention in the science mapping community. The present paper draws upon the use of the Stirling index as a diversity measure applied to a network model (customized science map) of research profiles, proposed by several authors. A modified version of the index is used and compared with the previous versions on a sample data set in order to rank top Hungarian research organizations (HROs) according to their research performance diversity. Results, unexpected in several respects, show that the modified index is a candidate for measuring the degree of polarization of a research profile. The study also points towards a possible typology of publication portfolios that instantiate different types of diversity

Bio-inspired Methods for Dynamic Network Analysis in Science Mapping

We apply bio-inspired methods for the analysis of different dynamic bibliometric networks (linking papers by citation, authors, and keywords, respectively). Biological species are clusters of individuals defined by widely different criteria and in the biological perspective it is natural to (1) use different categorizations on the same entities (2) to compare the different categorizations and to analyze the dissimilarities, especially as they change over time. We employ the same methodology to comparisons of bibliometric classifications. We constructed them as analogs of three species concepts: cladistic or lineage based, similarity based, and "biological species" (based on co-reproductive ability). We use the Rand and Jaccard indexes to compare classifications in different time intervals. The experiment is aimed to address the classic problem of science mapping, as to what extent the various techniques based on different bibliometric indicators, such as citations, keywords or authors are able to detect convergent structures in the litrerature, that is, to identify coherent specialities or research directions and their dynamics

Turning Negative Causation Back to Positive

In contemporary literature, the fact that there is negative causation is the primary motivation for rejecting the physical connection view, and arguing for alternative accounts of causation. In this paper we insist that such a conclusion is too fast. We present two frameworks, which help the proponent of the physical connection view to resist the anti-connectionist conclusion. According to the first framework, there are positive causal claims, which co-refer with at least some negative causal claims. According to the second framework, negative causal claims are generated from mapping and comparing different scenarios, which can fully be accounted for in purely positive terms. Since the positive causal claims evoked by both frameworks pose no obvious difficulties for the physical connection view, these frameworks make it possible for the connectionists to accommodate negative causal claims into their theory. Once these strategies are available, the connectionists become able to render all the arguments starting from the observation that there are negative causal claims in our causal discourse inconclusive with regard to the viability of the physical connection view

Business Networks. An Analysis of Influential Businessmen within the Network of the FIDESZ Party in Hungary

AbstractUnderstanding the relations of business and politics is of profound importance. The paper aims to analyze business-related connections among people within the network of FIDESZ, the currently leading political party in Hungary. The authors study the business networks within the boundaries of FIDESZ and identify the most influential businessmen using network research methodology. The main hypothesis, reflecting common knowledge, is that Lajos Simicska is a central point within this business network. The database of ahalo.hu has been used as the main source of data. First the sample FIDESZ business network is analyzed, followed by an analysis of the four biggest business communities identified in it. The article is concluded with the identification of the most influential businessmen of these. The findings of the article fail to support the main hypothesis: according to our findings (1) Péter Kiss, (2) Zoltán Varga, (3) Antal Nagy, (4) Márton Szabó, (5) János Bertalan, (6) Zsolt Nyerges, (7) Lajos Simicska and (8) István Töröcskei are considered to be the eight most influential businessmen of the FIDESZ network (and not Simicska is the most important). Qualitative research carried out in parallel to analyze the business activities of the aforementioned businessmen reveals that the current research can only partially point out the most prominent members of the FIDESZ business network

Analysis of Computational Science Papers from ICCS 2001-2016 using Topic Modeling and Graph Theory

This paper presents results of topic modeling and network models of topics using the International Conference on Computational Science corpus, which contains domain-specific (computational science) papers over sixteen years (a total of 5695 papers). We discuss topical structures of International Conference on Computational Science, how these topics evolve over time in response to the topicality of various problems, technologies and methods, and how all these topics relate to one another. This analysis illustrates multidisciplinary research and collaborations among scientific communities, by constructing static and dynamic networks from the topic modeling results and the keywords of authors. The results of this study give insights about the past and future trends of core discussion topics in computational science. We used the Non-negative Matrix Factorization topic modeling algorithm to discover topics and labeled and grouped results hierarchically.Comment: Accepted by International Conference on Computational Science (ICCS) 2017 which will be held in Zurich, Switzerland from June 11-June 1

SUPERCOMPUTER SIMULATION OF CRITICAL PHENOMENA IN COMPLEX SOCIAL SYSTEMS

The paper describes a problem of computer simulation of critical phenomena in complex social systems on a petascale computing systems in frames of complex networks approach. The three-layer system of nested models of complex networks is proposed including aggregated analytical model to identify critical phenomena, detailed model of individualized network dynamics and model to adjust a topological structure of a complex network. The scalable parallel algorithm covering all layers of complex networks simulation is proposed. Performance of the algorithm is studied on different supercomputing systems. The issues of software and information infrastructure of complex networks simulation are discussed including organization of distributed calculations, crawling the data in social networks and results visualization. The applications of developed methods and technologies are considered including simulation of criminal networks disruption, fast rumors spreading in social networks, evolution of financial networks and epidemics spreading