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A network is often an obvious choice for modeling real-life interconnected systems, where the nodes represent interacting objects and the edges represent their associations. There has been immense progress in complex network analysis with methods and tools that can provide important insights into the respective scenario. In the advancement of information technology and globalization, the amount of data is increasing day by day, and it is indeed incomprehensible without the help of network science. This work highlights how we can model multiple interaction scenarios under a single umbrella to uncover novel insights. We show that a varying scenario gets reflected by the change of topological patterns in interaction networks. We construct multi-scenario graphs, a novel framework proposed by us, from real-life environments followed by topological analysis. We focus on two different application areas: analyzing geographical variations in SARS-CoV-2 and studying topic similarity in citation patterns.

Additional file 1. Supplementary material which contains additional results and instruction manual of the software proposed in the main article.

Coronavirus Disease 2019 (COVID-19) has been creating a worldwide pandemic situation. Repurposing drugs, already shown to be free of harmful side effects, for the treatment of COVID-19 patients is an important option in launching novel therapeutic strategies. Therefore, reliable molecule interaction data are a crucial basis, where drug-/protein-protein interaction networks establish invaluable, year-long carefully curated data resources. However, these resources have not yet been systematically exploited using high-performance artificial intelligence approaches. Here, we combine three networks, two of which are year-long curated, and one of which, on SARS-CoV-2-human host-virus protein interactions, was published only most recently (30th of April 2020), raising a novel network that puts drugs, human and virus proteins into mutual context. We apply Variational Graph AutoEncoders (VGAEs), representing most advanced deep learning based methodology for the analysis of data that are subject to network constraints. Reliable simulations confirm that we operate at utmost accuracy in terms of predicting missing links. We then predict hitherto unknown links between drugs and human proteins against which virus proteins preferably bind. The corresponding therapeutic agents present splendid starting points for exploring novel host-directed therapy (HDT) options., Comment: 19 pages, 5 figures

We introduce a model of redistributive income taxation and public expenditure. This joint treatment permits analyzing the interdependencies between the two policies: one cannot be chosen independently of the other. Empirical evidence reveals that partisan confrontation essentially falls on expenditure policies rather than on income taxation. We examine the case in which the expenditure policy (or the size of government) is chosen by majority voting and income taxation is consistently adjusted. This adjustment consists of designing the income tax schedule that, given the expenditure policy, achieves consensus among the population. The model determines the consensus in- come tax schedule, the composition of public expenditure and the size of government. The main results are that inequality is negatively related to the size of government and to the pro-rich bias in public expenditure, and positively or negatively related to the marginal income tax, depending on substitutability between government supplied and market goods. These implications are validated using OECD data.

Specific functional forms are often used in economic models of distributions; goodness-of-fit measures are used to assess whether a functional form is appropriate in the light of real-world data. Standard approaches use a distance criterion based on the EDF, an aggregation of differences in observed and theoretical cumulative frequencies. However, an economic approach to the problem should involve a measure of the information loss from using a badly-fitting model. This would involve an aggregation of, for example, individual income discrepancies between model and data. We provide an axiomatisation of an approach and applications to illustrate its importance.