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.

András telcs 1,5,6,7 & János abonyi 4* the Erasmus Programme is the biggest collaboration network consisting of European Higher Education Institutions (HEIs). The flows of students, teachers and staff form directed and weighted networks that connect institutions, regions and countries. Here, we present a linked and manually verified dataset of this multiplex, multipartite, multi-labelled, spatial network. We enriched the network with institutional socioeconomic data from the European Tertiary Education Register (ETER) and the Global Research Identifier Database (GRID). We geocoded the headquarters of institutions and characterised the attractiveness and quality of their environments based on Points of Interest (POI) data. The linked datasets provide relevant information to grasp a more comprehensive understanding of the mobility patterns and attractiveness of the institutions.

Network analysis can be applied to understand organizations based on patterns of communication, knowledge flows, trust, and the proximity of employees. A multidimensional organizational network was designed, and association rule mining of the edge labels applied to reveal how relationships, motivations, and perceptions determine each other in different scopes of activities and types of organizations. Frequent itemset-based similarity analysis of the nodes provides the opportunity to characterize typical roles in organizations and clusters of co-workers. A survey was designed to define 15 layers of the organizational network and demonstrate the applicability of the method in three companies. The novelty of our approach resides in the evaluation of people in organizations as frequent multidimensional patterns of multilayer networks. The results illustrate that the overlapping edges of the proposed multilayer network can be used to highlight the motivation and managerial capabilities of the leaders and to find similarly perceived key persons.

To study education—occupation matchings we developed a bipartite network model of education to work transition and a graph configuration model based metric. We studied the career paths of 15 thousand Hungarian students based on the integrated database of the National Tax Administration, the National Health Insurance Fund, and the higher education information system of the Hungarian Government. A brief analysis of gender pay gap and the spatial distribution of over-education is presented to demonstrate the background of the research and the resulted open dataset. We highlighted the hierarchical and clustered structure of the career paths based on the multi-resolution analysis of the graph modularity. The results of the cluster analysis can support policymakers to fine-tune the fragmented program structure of higher education.

No abstract