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We propose a new centrality measure based on a self-adaptive epidemic model characterized by an endogenous reinforcement mechanism in the transmission of information between nodes. We provide a strategy to assign to nodes a centrality score that depends, in an eigenvector centrality scheme, on that of all the elements of the network, nodes and edges, connected to it. We parameterize this score as a function of a reinforcement factor, which for the first time implements the intensity of the interaction between the network of nodes and that of the edges. In this proposal, a local centrality measure representing the steady state of a diffusion process incorporates the global information encoded in the whole network. This measure proves effective in identifying the most influential nodes in the propagation of rumors/shocks/behaviors in a social network. In the context of financial networks, it allows us to highlight strategic assets on correlation networks. The dependence on a coupling factor between graph and line graph also enables the different asset responses in terms of ranking, especially on scale-free networks obtained as minimum spanning trees from correlation networks.

We propose a new paradigm to design a network-based self-adaptive epidemic model that relies on the interplay between the network and its line graph. We implement this proposal on a susceptible-infected-susceptible model in which both nodes and edges are considered susceptible and their respective probabilities of being infected result in a real-time re-modulation of the weights of both the graph and its line graph. The new model can be considered as an appropriate perturbation of the standard susceptible-infected-susceptible model, and the coupling between the graph and its line graph is interpreted as a reinforcement factor that fosters diffusion through a continuous adjustment of the parameters involved. We study the existence and stability conditions of the endemic and disease-free states for general network topologies. Moreover, we introduce, through the asymptotic values in the endemic steady states, a new type of eigenvector centrality where the score of a node depends on both the neighboring nodes and the edges connected to it. We also investigate the properties of this new model on some specific synthetic graphs, such as cycle, regular, and star graphs. Finally, we perform a series of numerical simulations and prove their effectiveness in capturing some empirical evidence on behavioral adoption mechanisms.

In this article, through the analysis of some essays of the so-called turning point, I will deal with the status and function of language in Heidegger. My interpretation is based on the following assumptions: 1) Being is the dif- ferential unity between itself “as such” and its historical indexing; 2) language represents the singular instance through which Being “as such” breaks into the domain of its indexing. It is an irruption that occurs in the mortal “act of speech” insofar as it re-says the Saying through a certain kind of circularity; 3) the very fact that the Saying necessarily avails itself of the mortal saying means that this saying decrees the essential groundlessness of both its own and that of the Being that avails itself of it.

Celle du sujet représente la notion centrale de la philosophie moderne. Au carrefour des catégories althussériennes (de l’interpellation et de l’idéologie) et lacaniennes (du signifiant comme marque de subjectivation), l’analyse du discours, en particulier celle de Michel Pêcheux, a produit l’une des théories les plus originales concernant le sujet. Dans cet article, j’explorerai le processus de subjectivation à partir de la notion de préconstruit. Plus précisément, dans un premier temps, j’accorderai une attention particulière à la notion de présupposition élaborée par Oswald Ducrot dans son ouvrage Dire et ne pas dire, afin de mettre en évidence le fait que les présuppositions sémantiques doivent être considérées comme des types d’actes illocutoires. Dans la deuxième partie de cette étude, je développerai cette nature illocutoire et analyserai l’essai de Searle, A Taxonomy of Illocutionary Acts. À partir de la notion searléienne d’un vecteur d’adaptation entre les mots et le monde et entre le monde et les mots, je montrerai comment l’interpellation revêt une forme déclarative. Ces avancées théoriques me permettront d’énoncer cinq thèses sur le sujet.

The energy consumption, the transfer of resources through the international trade, the transition towards renewable energies and the environmental sustainability appear as key drivers in order to evaluate the resilience of the energy systems. Concerning the consumptions, in the literature a great attention has been paid to direct energy, but the production of goods and services also involves indirect energy. Hence, in this work we consider different types of embodied energy sources and the time evolution of the sectors' and countries' interactions. Flows are indeed used to construct a directed and weighted temporal multilayer network based respectively on renewable and non-renewable sources, where sectors are nodes and layers are countries. We provide a methodological approach for analysing the network reliability and resilience and for identifying critical sectors and economies in the system by applying the Multi-Dimensional HITS algorithm. Then, we evaluate central arcs in the network at each time period by proposing a novel topological indicator based on the maximum flow problem. In this way, we provide a full view of economies, sectors and connections that play a relevant role over time in the network and whose removal could heavily affect the stability of the system. We provide a numerical analysis based on the embodied energy flows among countries and sectors in the period from 1990 to 2016. Results prove that the methods are effective in catching the different patterns between renewable and non-renewable energy sources.

Apparso nel 1966, Pour une théorie de la production littéraire di Pierre Macherey rappresenta ancora oggi una ricerca imprescindibile per chi desideri avvicinarsi alla teoria della letteratura e agli studi sulla critica dell’ideologia. Nato nella cornice dell’althusserismo, il libro di Macherey intende sfidare il mito dell’unità dell’opera letteraria. Cosa che si potrebbe riassumere con la formulazione: l’opera è sempre situata, ovvero, essa mette in opera una situazione che definisce ed è definita dalla totalità delle relazioni discorsive in cui essa è presa. È una tesi impegnativa che, tra le altre cose, riformula completamente l’interrogativo sartriano circa l’essenza della letteratura in quello relativo a quali siano le sue condizioni di produzione. Lo scrittore, infatti, non è più un creatore che sta davanti alla sua creazione, ma un artigiano che lavora il “suo” testo a partire da determinate condizioni discorsive.

In this paper, we investigate the mesoscale structure of the World Trade Network. In this framework, a specific role is assumed by short- and long-range interactions, and hence by any suitably defined network-based distance between countries. Therefore, we identify clusters through a new procedure that exploits Estrada communicability distance and the vibrational communicability distance, which turn out to be particularly suitable for catching the inner structure of the economic network. The proposed methodology aims at finding the distance threshold that maximizes a specific quality function defined for general metric spaces. Main advantages regard the computational efficiency of the procedure as well as the possibility to inspect intercluster and intracluster properties of the resulting communities. The numerical analysis highlights peculiar relationships between countries and provides a rich set of information that can hardly be achieved within alternative clustering approaches.

In this study, I hypothetically trace some vanishing point for the analysis of discourse as it was developed by Michel Pêcheux in the period between the 1960s and 1970s. In order to do that, I refer to the analyses that Maurice Merleau-Ponty devoted to linguistic facts, in particular to literary speech. The use of the Cours de linguistique générale by these two authors constitutes the leverage point for developing the analysis of three orientations: the semantic orientation; the subjective orientation; the ontological orientation. In the course of this analysis, we will see how the theoretical positions of Pêcheux and Merleau-Ponty, although opposed, can be put in tension. I will measure their relationship through a certain number of theses.

In this paper, we aim at investigating various aspects of countries’ behaviour during the coronavirus pandemic period. By means of a multiplex network, we simultaneously consider government responses based on COVID-19 infections, Stringency Index, international trade and international air mobility data, to detect clusters of countries that showed a similar reaction to the pandemic. We propose a new methodological approach based on the Estrada communicability for identifying communities on a multiplex network, based on a two-step optimization process. At first, we determine the optimal inter-layer weight between levels by minimizing a distance function. Hence, the optimal weight is used to detect the communities on each layer. Our findings show that this new approach to community detection on a multiplex network provides additional insights with respect to the same procedure performed on layers separately. Indeed, clusters in the multiplex network benefit from a higher cohesion, as they are detected taking into account the mutual influence of the other networks.

The challenges of using technology in the insurance field are opening new horizons for developing and distributing innovative products. Among these, peer-to-peer insurance schemes attract the interest of policyholders and insurance companies. Different types of peer-to-peer insurance have been introduced, from pure models to hybrid ones, as in the case of the broker model. In this paper, we focus on the broker model, where the groups of peers are formed by an insurance broker according to similar risk characteristics. The participants in the network pay an initial contribution defined by a cooperative rule that must be transparent and shared. A part or the whole of the collected contributions is set aside in a common fund. At the end of the year, if the common fund is sufficient to pay for the claims, the members obtain the excess over-retained premiums that is shared according to a capital allocation rule. We propose a cashback distribution mechanism based on the participant’s marginal contribution to the risk, framing the issue in a cooperative game and applying the concept of Shapley value to define an optimal allocation rule of the remaining capital. A numerical application based on a portfolio of motor third-party liability policies is developed to show how the model works

In the complex global food system, the dynamics associated with international food trade have become crucial determinants of food security. In this paper, we employ a community detection approach along with a supervised learning technique to explore the evolution of communities in the agri-food trade network and to identify key factors influencing their composition. By leveraging a large dataset that includes both volume and monetary value of trades, we identify similarities between countries and uncover the primary drivers that shape trade dynamics over time. The analysis also takes into account the impact of evolving climate conditions on food production and trading. The results highlight how the network’s topological structure is continuously evolving, influencing the composition of communities over time. Alongside geographical proximity and geo-political relations, our analysis identifies sustainability, climate and food nutrition aspects as emerging factors that contribute to explaining trade relationships. These findings shed light on the intricate interactions within the global food trade system and provide valuable insights into the factors affecting its stability.

We provide a framework for detecting relevant insurance companies in a systemic risk perspective. Among the alternative methodologies for measuring systemic risk, we propose a complex network approach where insurers are linked to form a global interconnected system. We model the reciprocal influence between insurers calibrating edge weights on the basis of specific risk measures. Therefore, we provide a suitable network indicator, the Weighted Effective Resistance Centrality, able to catch which is the effect of a specific vertex on the network robustness. By means of this indicator, we assess the prominence of a company in spreading and receiving risk from the others.

Within the insurance field, the digital revolution has enabled the collection and storage of large quantities of information. This era is referred to as “big data”, since the great uncertainty to be modelled is too complex for traditional data processing techniques. For insurance purposes, big data refers to unstructured and/or structured data being used to influence underwriting, rating, pricing, forms, marketing and claims handling. The use of artificial intelligence (AI) in actuarial science is a rapidly growing area that is significantly impacting the insurance industry. This integration of technology involves both machine learning (ML) algorithms and data analysis to help actuaries develop more accurate risk assessments and predictions. With a larger possibility of accessing huge sets of data, actuaries can leverage AI to automate underwriting and claims processing, improve customer experience and provide valuable insights to insurers to perfect decision-making strategies. In this way, AI has the potential to transform traditional actuarial methodologies and provide customized solutions for individual clients. In particular, the field of insurance has greatly benefited from the application of data science. With the use of data analytics, insurance companies can now develop predictive models to better manage risk, underwrite policies more accurately and determine valid pricing. Data science is also used to automate specific procedures such as claims processing, fraud detection, and customer retention. Insurance companies may use data science to identify and build new products that better match clients’ needs and preferences. In addition, the accessibility of data can improve operational efficiency by identifying areas of improvement and revenue-generating opportunities. Overall, the use of data science is quickly becoming a crucial aspect of the insurance industry, helping insurers improve their decision making, create new products and services and enhance custo

Clustering and closure coefficients are among the most widely applied indicators in the description of the topological structure of a network. Many distinct definitions have been proposed over time, particularly in the case of weighted networks, where the choice of the weight attributed to the triangles is a crucial aspect. In the present work, in the framework of weighted directed multilayer networks, we extend the classical clustering and closure coefficients through the introduction of the clumping coefficient, which generalizes them to incomplete triangles of any type. We then organize the class of these coefficients in a systematic taxonomy in the more general context of weighted directed multilayer networks. Such cohesion coefficients have also been adapted to the different scales that characterize a multilayer network, in order to grasp their structure from different perspectives. We also show how the tensor formalism allows incorporating the new definitions, as well as all those existing in the literature, in a single unified writing, in such a way that a suitable choice of the involved adjacency tensors allows obtaining each of them. Finally, through some applications to simulated networks, we show the effectiveness of the proposed coefficients in capturing different peculiarities of the network structure on different scales.

In this paper, we provide novel definitions of clustering coefficient for weighted and directed multilayer networks. We extend in the multilayer theoretical context the clustering coefficients proposed in the literature for weighted directed monoplex networks. We quantify how deeply a node is involved in a cohesive structure focusing on a single node, a single layer or the entire system, respectively. The coefficients convey several characteristics inherent to the complex topology of the multilayer network. We test their effectiveness applying them to a particularly complex structure such as the international trade network. The trade data integrate different aspects and they can be described by a directed and weighted multilayer network, where each layer represents import and export relationships between countries for a given sector. The proposed coefficients find successful application in describing the interrelations of the trade network, allowing to disentangle the effects of countries and sectors and jointly consider the interactions between them.

In this work we analyse the global soccer player transfer market providing a network approach that takes into account both the number of transfers and the related costs for football players in the world market. We propose a community detection methodology that considers different features of the network. We cluster countries according to similarities in their roles in the transfer market and to the presence of indirect connections due to common neighbours. Numerical results show a strict relation between the composition of clusters and the economic value of the football leagues of different countries. Indeed, we observe that, on average, leagues with a similar economic value belongs to the same cluster. The analysis has been also extended providing a comparison based on the world trade network. We observe that prominent European players in the economic trades are also relevant in the soccer transfer network.

We present a variational quantum eigensolver (VQE) algorithm for the efficient bootstrapping of the causal representation of multiloop Feynman diagrams in the loop-tree duality or, equivalently, the selection of acyclic configurations in directed graphs. A loop Hamiltonian based on the adjacency matrix describing a multiloop topology, and whose different energy levels correspond to the number of cycles, is minimized by VQE to identify the causal or acyclic configurations. The algorithm has been adapted to select multiple degenerated minima and thus achieves higher detection rates. A performance comparison with a Grover's based algorithm is discussed in detail. The VQE approach requires, in general, fewer qubits and shorter circuits for its implementation, albeit with lesser success rates.

In this paper, we aim at investigating various aspects of countries' behaviour during the coronavirus pandemic period. By means of a multiplex network, we simultaneously consider government responses based on COVID-19 infections, Stringency Index, international trade and international air mobility data, to detect clusters of countries that showed a similar reaction to the pandemic. We propose a new methodological approach based on the Estrada communicability for identifying communities on a multiplex network, based on a two-step optimization process. At first, we determine the optimal inter-layer weight between levels by minimizing a distance function. Hence, the optimal weight is used to detect the communities on each layer. Our findings show that this new approach to community detection on a multiplex network provides additional insights with respect to the same procedure performed on layers separately. Indeed, clusters in the multiplex network benefit from a higher cohesion, as they are detected taking into account the mutual influence of the other networks.