We investigated competitive conditions in global value chains (GVCs) for a period of fifteen years (2000–2014), focusing on sector structure, countries’ dominance and diversification. For this purpose, we used data from the World Input–Output Database (WIOD) and examined GVCs as weighted directed networks, where countries are the nodes and value added flows are the edges. We compared the in-and out-weighted degree centralization of the sectoral GVC networks in order to detect the most centralized, on the import or export side, respectively (oligopsonies and oligopolies). Moreover, we examined the in- and out-weighted degree centrality and the in- and out-weight entropy in order to determine whether dominant countries are also diversified. The empirical results reveal that diversification (entropy) and dominance (degree) are not correlated. Dominant countries (rich) become more dominant (richer). Diversification is not conditioned by competitiveness.

The global economy operates as a complex and interconnected system, necessitating the application of sophisticated network methods for analysis. This study examines economic data from all countries across the globe, representing each country as a node and its exports as links, covering the period from 2008 to 2019. Through the computation of relevant indices, we can discern shifts in countries’ positions within the world trade network. By interpreting these changes through geopolitical perspectives, we can gain a deeper understanding of their root causes. The analysis reveals a notable trend of slow growth in the world trade network. Additionally, an intriguing observation emerges: countries naturally form stable groups, shedding light on the underlying structure of global trade relations. Furthermore, this research highlights the trade balance as a reflection of geopolitical strength, making it a valuable contribution to the study of the evolution of global geopolitical stability.

The COVID-19 pandemic caused a boom in demand for personal protective equipment, or so-called “COVID-19 goods”, around the world. We investigate three key sectoral global value chain networks, namely, “chemicals”, “rubber and plastics”, and “textiles”, involved in the production of these goods. First, we identify the countries that export a higher value added share than import, resulting in a “value added surplus”. Then, we assess their value added flow diversification using entropy. Finally, we analyze their egonets in order to identify their key affiliates. The relevant networks were constructed from the World Input-Output Database. The empirical results reveal that the USA had the highest surplus in “chemicals”, Japan in “rubber and plastics”, and China in “textiles”. Concerning value added flows, the USA was highly diversified in “chemicals”, Germany in “rubber and plastics”, and Italy in “textiles”. From the analysis of egonets, we found that the USA was the key supplier in all sectoral networks under consideration. Our work provides meaningful conclusions about trade outperformance due to the fact of surplus, trade flow robustness due to the fact of diversification, and trade partnerships due to the egonets analysis.

Red Flags in fiscal projects are warning signs that may indicate underlying problems with their implementation. In this paper, we present how National Strategic Reference Framework Open Data can be used to take full advantage of semantic web technologies and data mining techniques to build a knowledge-based system that identifies Red Flags. We collected the data from the Open Data API provided by the Greek Ministry of Economy and Finance. Data modeling consist of two ontologies; the Vocabulary of Fiscal Projects, describing the fiscal projects and the National Strategic Reference Framework Greece Vocabulary, illustrating the Greek National Strategic Reference Framework data. We transformed the data into RDF triples and uploaded them onto an OpenLink Virtuoso Server, so that we could retrieve them via SPARQL queries. Performance indicators were defined to assess the state of the project and Density-Based Spatial Clustering of Applications with Noise, (DBSCAN) was used to identify Red Flags. User’s demands is that rejected projects should raise Red Flags, to avoid project failure and assist the auditor to organize the monitoring process efficiently, by avoiding to examine most of the non-problematic projects. We performed a use case scenario in which an auditor has to examine NSRF projects, approximately 12 months before the end of the programming period. The system retrieved the fiscal information, calculated the performance indicators and identified the Red Flags. The last update of the projects status after the end of the programming period was retrieved and extracted the number of rejected projects, to test whether the user requirements are satisfied. Rejected projects consist of 3.8% of the total projects. The results of the use case scenario show that RedFlags platform is more likely to identify project failures and not raise Red Flags on not rejected projects. Therefore, the RedFlags platform using open data, assists the auditor to organize the monitoring process better.

We extend network analysis to directed criminal networks in the context of asymmetric links. We computed selected centralities, centralizations and the assortativity of a drug trafficking network with 110 nodes and 295 edges. We also monitored the centralizations of eleven temporal networks corresponding to successive stages of investigation during the period 1994–1996. All indices reach local extrema at the stage of highest activity, extending previous results to directed networks. The sharpest changes ($90\%$) are observed for betweenness and in-degree centralization. A notable difference between entropies is observed: the in-degree entropy reaches a global minimum at month 12, while the out-degree entropy reaches a global maximum. This confirms that at the stage of highest activity, incoming instructions are precise and focused, while outgoing instructions are diversified. These findings are expected to be useful for alerting the authorities to increasing criminal activity. The disruption simulations on the time-averaged network extend previous results on undirected networks to directed networks.