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Greece constitutes a coastal country with a lot of geomorphologic, climatic, cultural and historic peculiarities favoring the development of many aspects of tourism. Within this framework, this article examines what are the effects of tourism in Greece and how determinative these effects are, by applying a macroscopic analysis on empirical data for the estimation of the contribution of tourism in the Greek Economy. The available data regard records of the Balance of Payments in Greece and of the major components of the Balance of the Invisible Revenues, where a measurable aspect of tourism, the Travel or Tourism Exchange, is included. At the time period of the available data (2000-2012) two events of the recent Greek history are distinguished as the most significant (the Olympic Games in the year 2004 and the economic crisis initiated in the year 2009) and their impact on the diachronic evolution in the tourism is discussed. Under an overall assessment, the analysis illustrated that tourism is a sector of the Greek economy, which is described by a significant resilience, but it seems that it has not yet been submitted to an effective developmental plan exploiting the endogenous tourism dynamics of the country, suggesting currently a promising investment of low risk for the economic growth of country and the exit of the economic crisis

The study and measurement of economic resilience is ruled by high level of complexity related to the diverse structure, functionality, spatiality, and dynamics describing economic systems. Towards serving the demand of integration, this paper develops a three-dimensional index, capturing engineering, ecological, and evolutionary aspects of economic resilience that are considered separately in the current literature. The proposed index is computed on GDP data of worldwide countries, for the period 1960-2020, concerning 14 crises considered as shocks, and was found well defined in a conceptual context of its components. Its application on real-world data allows introducing a novel classification of countries in terms of economic resilience, and reveals geographical patterns and structural determinants of this attribute. Impressively enough, economic resilience appears positively related to major productivity coefficients, gravitationally driven, and depended on agricultural specialization, with high structural heterogeneity in the low class. Also, the analysis fills the literature gap by shaping the worldwide map of economic resilience, revealing geographical duality and centrifugal patterns in its geographical distribution, a relationship between diachronically good performance in economic resilience and geographical distance from the shocks origin, and a continent differentiation expressed by the specialization of America in engineering resilience, Africa and Asia in ecological and evolutionary resilience, and a relative lag of Europe and Oceania. Finally, the analysis provides insights into the effect of the 2008 on the globe and supports a further research hypothesis that political instability is a main determinant of low economic resilience, addressing avenues of further research.

Using network analysis, this paper develops a multidimensional methodological framework for understanding the uneven (cross-country) spread of COVID-19 in the context of the global interconnected economy. The globally interconnected system of tourism mobility is modeled as a complex network, where two main stages in the temporal spread of COVID-19 are revealed and defined by the cutting-point of the 44th day from Wuhan. The first stage describes the outbreak in Asia and North America, the second one in Europe, South America, and Africa, while the outbreak in Oceania is spread along both stages. The analysis shows that highly connected nodes in the global tourism network (GTN) are infected early by the pandemic, while nodes of lower connectivity are late infected. Moreover, countries with the same network centrality as China were early infected on average by COVID-19. The paper also finds that network interconnectedness, economic openness, and transport integration are key determinants in the early global spread of the pandemic, and it reveals that the spatio-temporal patterns of the worldwide spread of COVID-19 are more a matter of network interconnectivity than of spatial proximity.

This paper proposes a new method for converting a time-series into a weighted graph (complex network), which builds on the electrostatic conceptualization originating from physics. The proposed method conceptualizes a time-series as a series of stationary, electrically charged particles, on which Coulomb-like forces can be computed. This allows generating electrostatic-like graphs associated to time-series that, additionally to the existing transformations, can be also weighted and sometimes disconnected. Within this context, the paper examines the structural relevance between five different types of time-series and their associated graphs generated by the proposed algorithm and the visibility graph, which is currently the most established algorithm in the literature. The analysis compares the source time-series with the network-based node-series generated by network measures that are arranged into the node-ordering of the source time-series, in terms of linearity, chaotic behaviour, stationarity, periodicity, and cyclical structure. It is shown that the proposed electrostatic graph algorithm produces graphs that are more relevant to the structure of the source time-series by introducing a transformation that converts the time-series to graphs. This is more natural rather than algebraic, in comparison with existing physics-defined methods. The overall approach also suggests a methodological framework for evaluating the structural relevance between the source time-series and their associated graphs produced by any possible transformation.

The global crisis caused by the COVID-19 pandemic, in conjunction with the economic consequences and the collapse of health systems, has raised serious concerns in Europe, which is the most affected continent by the pandemic since it recorded 2,388,694 cases and 190,091 deaths (39.6% of the worldwide total), of which 71.7% (136,238) are in the United Kingdom (43,414), Italy (34,708), France (29,778), and Spain (28,338). Unlike other countries, Greece, with about 310 confirmed cases and 18 deaths per million, is one bright exception in the study and analysis of this phenomenon. Focusing on the peculiarities of the disease spreading in Greece, both in epidemiological and in implementation terms, this paper applies an exploratory analysis of COVID-19 temporal spread in Greece and proposes a methodological approach for the modeling and prediction of the disease based on the Regression Splines algorithm and the change rate of the total infections. Also, it proposes a hybrid spline regression and complex network model of social distance measures evaluating and interpreting the spread of the disease. The overall approach contributes to decision making and support of the public health system and to the fight against the pandemic.

Within the complex framework of anti-COVID-19 health management, where the criteria of diagnostic testing, the availability of public-health resources and services, and the applied anti-COVID-19 policies vary between countries, the reliability and the accuracy in the modeling of temporal spread can be proven effective in the worldwide fight against the disease. This paper applies an exploratory time-series analysis to the evolution of the disease in Greece, which currently suggests a success story of COVID-19 management. The proposed method builds on a recent conceptualization of detecting connective communities in a time-series and develops a novel spline regression model where the knot vector is determined by the community detection in the complex network. Overall, the study contributes to the COVID-19 research by proposing a free of disconnected past-data and reliable framework of forecasting, which can facilitate decision-making and management of the available health resources.

Within the context that Greece promises a success story in the fight against the disease, this paper proposes a novel method to study the evolution of the Greek COVID-19 infection-curve in relation to the anti-COVID-19 policies applied to control the pandemic. Based on the ongoing spreading of COVID-19 and the insufficient data for applying classic time-series approaches, the analysis builds on the visibility graph algorithm to study the Greek COVID-19 infection-curve as a complex network. By using the modularity optimization algorithm, the generated visibility graph is divided into communities defining periods of different connectivity in the time-series body. These periods reveal a sequence of different typologies in the evolution of the disease, starting with a power pattern, where a second order polynomial (U-shaped) pattern intermediates, being followed by a couple of exponential patterns, and ending up with a current logarithmic pattern revealing that the evolution of the Greek COVID-19 infection-curve tends into saturation. The network analysis also illustrates stability of hubs and instability of medium and low-degree nodes, implying a low probability to meet maximum (infection) values at the future and high uncertainty in the variability of other values below the average. The overall approach contributes to the scientific research by proposing a novel method for the structural decomposition of a time-series into periods, which allows removing from the series the disconnected past-data facilitating better forecasting, and provides insights of good policy and decision-making practices and management that may help other countries improve their performance in the war against COVID-19.

This article studies the interregional Greek road network (GRN) by applying complex network analysis (CNA) and an empirical approach. The study aims to extract the socioeconomic information immanent to the GRN's topology and to interpret the way in which this road network serves and promotes the regional development. The analysis shows that the topology of the GRN is submitted to spatial constraints, having lattice-like characteristics. Also, the GRN's structure is described by a gravity pattern, where places of higher population enjoy greater functionality, and its interpretation in regional terms illustrates the elementary pattern expressed by regional development through road construction. The study also reveals some interesting contradictions between the metropolitan and non-metropolitan (excluding Attica and Thessaloniki) comparison. Overall, the article highlights the effectiveness of using complex network analysis in the modeling of spatial networks and in particular of transportation systems and promotes the use of the network paradigm in the spatial and regional research., Comment: in Greek

This article studies the Greek interregional commuting network (GRN) by using measures and methods of complex network analysis and empirical techniques. The study aims to detect structural characteristics of the commuting phenomenon, which are configured by the functionality of the land transport infrastructures, and to interpret how this network serves and promotes the regional development. In the empirical analysis, a multiple linear regression model for the number of commuters is constructed, which is based on the conceptual framework of the term network, in effort to promote the interdisciplinary dialogue. The analysis highlights the effect of the spatial constraints on the network's structure, provides information on the major road transport infrastructure projects that constructed recently and influenced the country capacity, and outlines a gravity pattern describing the commuting phenomenon, which expresses that cities of high population attract large volumes of commuting activity within their boundaries, a fact that contributes to the reduction of their outgoing commuting and consequently to the increase of their inbound productivity. Overall, this paper highlights the effectiveness of complex network analysis in the modeling of spatial and particularly of transportation network and promotes the use of the network paradigm in the spatial and regional research., Comment: in Greek

This article attempts to highlight the importance that transportation has in the economic development of Greece and in particular the importance of the transportation infrastructure and transportation networks, which suggest a fixed structured capital covering the total of the country. For this purpose, longitudinal and cross-sectoral statistical data are examined over a set of fundamental macroeconomic measures and metrics. Furthermore, the study attempts to highlight the structural and functional aspects composing the concept of transportation networks and to highlight the necessity of their joint consideration on the relevant research. The transportation networks that are examined in this paper are the Greek road (GRN), rail (GRAN), maritime (GMN) and air transport network (GAN), which are studied both in terms of their geometry and technical characteristics, as well as of their historical, traffic and political framework. For the empirical assessment of the transportation networks importance in Greece an econometric model is constructed, expressing the welfare level of the Greek regions as a multivariate function of their transportation infrastructure and of their socioeconomic environment. The further purpose of the article is to highlight, macroscopically, all the aspects related the study of transportation infrastructure and networks., Comment: in Greek

Technological developments worldwide are contributing to the improvement of transport infrastructures and they are helping to reduce the overall transport costs. At the same time, such developments along with the reduction in transport costs are affecting the spatial interdependence between the regions and countries, a fact inducing significant effects on their economies and, in general, on their growth-rates. A specific class of transport infrastructures contributing significantly to overcoming the spatial constraints is the airtransport infrastructures. Nowadays, the importance of air-transport infrastructures in the economic development is determinative, especially for the geographically isolated regions, such as for the island regions of Greece. Within this context, this paper studies the Greek airports and particularly the evolution of their overall transportation imprint, their geographical distribution, and the volume of the transport activity of each airport. Also, it discusses, in a broad context, the seasonality of the Greek airport activity, the importance of the airports for the local and regional development, and it formulates general conclusions., Comment: in Greek

Network Science is an emerging discipline using the network paradigm to model communication systems as pair-sets of interconnected nodes and their linkages (edges). This paper applies this paradigm to study an interacting system in regional economy consisting of daily road transportation flows for labor purposes, the so-called commuting phenomenon. In particular, the commuting system in Greece including 39 non-insular prefectures is modeled into a complex network and it is studied using measures and methods of complex network analysis and empirical techniques. The study aims to detect the structural characteristics of the Greek interregional commuting network (GCN) and to interpret how this network is related to the regional development. The analysis highlights the effect of the spatial constraints in the structure of the GCN, it provides insights about the major road transport projects constructed the last decade, and it outlines a populationcontrolled (gravity) pattern of commuting, illustrating that high-populated regions attract larger volumes of the commuting activity, which consequently affects their productivity. Overall, this paper highlights the effectiveness of complex network analysis in the modeling of systems of regional economy, such as the systems of spatial interaction and the transportation networks, and it promotes the use of the network paradigm to the regional research.

This paper expands the degree-based consideration of the preferential attachment growth process and applies five different connectivity criteria (node degree, clustering coefficient, betweenness centrality, closeness centrality, and eigenvector centrality) to define the development of new links in the networks. Based on statistical inference, the analysis shows that all the available control attributes are capable generating SF networks, that the proposed generalized preferential attachment growth process produces networks of statistically different topologies, under different control-attributes, and that the betweenness centrality is the control-attribute generating networks of better topology. Overall, this paper introduces a multi-dimensional conceptualization of preferential attachment, which can motivate further research and can provide new tools for the modeling and interpretation of real-world networks that currently cannot be fully explained by the degree-driven BA models.