An analysis of emotion-exchange motifs in multiplex networks during emergency events

In this paper, we present an analysis of the emotion-exchange patterns that arise from Twitter messages sent during emergency events. To this end, we performed a systematic structural analysis of the multiplex communication network that we derived from a data-set including more than 1.9 million tweets that have been sent during five recent shootings and terror events. In order to study the local communication structures that emerge as Twitter users directly exchange emotional messages, we propose the concept of emotion-exchangemotifs. Our findings suggest that emotion-exchange motifs which contain reciprocal edges (indicating online conversations) only emerge when users exchange messages that convey anger or fear, either in isolation or in any combination with another emotion. In contrast, the expression of sadness, disgust, surprise, as well as any positive emotion are rather characteristic for emotion-exchange motifs representing one-way communication patterns (instead of online conversations). Among other things, we also found that a higher structural similarity exists between pairs of network layers consisting of one high-arousal emotion and one low-arousal emotion, rather than pairs of network layers belonging to the same arousal dimension

Impact of natural disasters on consumer behavior: Case of the 2017 El Niño phenomenon in Peru

El Niño is an extreme weather event featuring unusual warming of surface waters in the eastern equatorial Pacific Ocean. This phenomenon is characterized by heavy rains and floods that negatively affect the economic activities of the impacted areas. Understanding how this phenomenon influences consumption behavior at different granularity levels is essential for recommending strategies to normalize the situation. With this aim, we performed a multi-scale analysis of data associated with bank transactions involving credit and debit cards. Our findings can be summarized into two main results: Coarse-grained analysis reveals the presence of the El Niño phenomenon and the recovery time in a given territory, while fine-grained analysis demonstrates a change in individuals’ purchasing patterns and in merchant relevance as a consequence of the climatic event. The results also indicate that society successfully withstood the natural disaster owing to the economic structure built over time. In this study, we present a new method that may be useful for better characterizing future extreme events

Complex Urban Systems: Challenges and Integrated Solutions for the Sustainability and Resilience of Cities

For decades, from design theory to urban planning and management, from social sciences to urban environmental science, cities have been probed and analyzed from the partial perspective of single disciplines. The digital era, with its unprecedented data availability, is allowing for testing old theories and developing new ones, ultimately challenging relatively partial models. Our community has been in the last years providing more and more compelling evidence that cities are complex systems with emergent phenomena characterized by the collective behavior of their citizens who are themselves complex systems. However, more recently, it has also been shown that such multiscale complexity alone is not enough to describe some salient features of urban systems. Multilayer network modeling, accounting for both multiplexity of relationships and interdependencies among the city's subsystems, is indeed providing a novel integrated framework to study urban backbones, their resilience to unexpected perturbations due to internal or external factors, and their human flows. In this paper, we first offer an overview of the transdisciplinary efforts made to cope with the three dimensions of complexity of the city: the complexity of the urban environment, the complexity of human cognition about the city, and the complexity of city planning. In particular, we discuss how the most recent findings, for example, relating the health and wellbeing of communities to urban structure and function, from traffic congestion to distinct types of pollution, can be better understood considering a city as a multiscale and multilayer complex system. The new challenges posed by the postpandemic scenario give to this perspective an unprecedented relevance, with the necessity to address issues of reconstruction of the social fabric, recovery from prolonged psychological, social and economic stress with the ensuing mental health and wellbeing issues, and repurposing of urban organization as a consequence of new emerging practices such as massive remote working. By rethinking cities as large-scale active matter systems far from equilibrium which consume energy, process information, and adapt to the environment, we argue that enhancing social engagement, for example, involving citizens in codesigning the city and its changes in this critical postpandemic phase, can trigger widespread adoption of good practices leading to emergent effects with collective benefits which can be directly measured

Computational socioeconomics

Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies

EUSN 2021 Book of Abstracts, Fifth European Conference on Social Networks

Book of abstract of the fifth European conference on Social Networks EUSN 202

An Initial Framework Assessing the Safety of Complex Systems

Trabajo presentado en la Conference on Complex Systems, celebrada online del 7 al 11 de diciembre de 2020.Atmospheric blocking events, that is large-scale nearly stationary atmospheric pressure patterns, are often associated with extreme weather in the mid-latitudes, such as heat waves and cold spells which have significant consequences on ecosystems, human health and economy. The high impact of blocking events has motivated numerous studies. However, there is not yet a comprehensive theory explaining their onset, maintenance and decay and their numerical prediction remains a challenge. In recent years, a number of studies have successfully employed complex network descriptions of fluid transport to characterize dynamical patterns in geophysical flows. The aim of the current work is to investigate the potential of so called Lagrangian flow networks for the detection and perhaps forecasting of atmospheric blocking events. The network is constructed by associating nodes to regions of the atmosphere and establishing links based on the flux of material between these nodes during a given time interval. One can then use effective tools and metrics developed in the context of graph theory to explore the atmospheric flow properties. In particular, Ser-Giacomi et al. [1] showed how optimal paths in a Lagrangian flow network highlight distinctive circulation patterns associated with atmospheric blocking events. We extend these results by studying the behavior of selected network measures (such as degree, entropy and harmonic closeness centrality)at the onset of and during blocking situations, demonstrating their ability to trace the spatio-temporal characteristics of these events.This research was conducted as part of the CAFE (Climate Advanced Forecasting of sub-seasonal Extremes) Innovative Training Network which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813844

Human mobility: Models and applications

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordRecent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between short-range and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications.US Army Research Offic

Essays on the Network Analysis of Culture

Nelle relazioni economiche, negli accordi internazionali e nel dialogo istituzionale, la parola distanza \ue8 una delle pi\uf9 enunciate. Ci sono distanze esogene da colmare per creare legami, a volte ci sono chiusure necessarie e altre volte rotture inevitabili, ma questo pu\uf2 dipendere, cos\uec come le distanze geografiche e fisiche, e gli interessi impliciti, in gran parte dallo status culturale di gruppi di individui. La valutazione quantitativa della distanza tra due entit\ue0 \ue8 una propriet\ue0 diadica ed in quanto tale, la presenza, intensit\ue0, direzione e segno di un legame rappresenta un modo per catturarla. Poich\ue9 le entit\ue0 possono essere individui, oggetti, societ\ue0, paesi, pianeti, cos\uec come reti che si riferiscono a contesti specifici, e il modo di misurare la somiglianza tra di loro pu\uf2 essere vario, una cosa peculiare delle distanze \ue8 la loro natura mutevole. Mentre le distanze fisiche sono quasi oggettivamente calcolabili, nel caso della cultura (ed anche di altri concetti pi\uf9 o meno ampi) l\u2019utilizzo di un metodo rispetto ad un altro potrebbe cambiare radicalmente la relazione di distanza tra le entit\ue0, soprattutto se esse hanno un alto grado di complessit\ue0. Il bagaglio culturale svolge un ruolo importante nel determinare lo status socio-economico di un paese e la sua caratterizzazione in termini di somiglianza con altri paesi. Il Capitolo 1 - utilizzando i dati della WVS/EVS Joint 2017 - operativizza una definizione di cultura che tiene conto delle interdipendenze tra tratti culturali a livello di paese e propone una nuova misura di distanza culturale. Sfruttando un recente algoritmo Bayesiano di Copula Gaussian graphical models, questo Capitolo stima per ciascuno di 76 paesi inclusi nella WVS/EVS Joint 2017, la rete culturale di interdipendenze tra tratti culturali considerando diversi insiemi di essi: i 6 della prima batteria di domande, i 10 della mappa culturale di Inglehart-Welzel, i 14 della mappa culturale di Inglehart-Welzel, dove per gli indici di \u201cPost-materialism\u201d e \u201cAutonomy\u201d sono state utilizzate le variabili da cui sono ricavate, e 60 tratti culturali dei quali, 14 come definiti in precedenza, 6 fanno riferimento alla prima batteria di domande e i restanti 40 sono selezionati in modo da ottenere un numero di variabili che possa far fronte al trade-off tra il tempo di elaborazione dell\u2019algoritmo e il minimo numero di valori mancanti per paese. Dopo aver definito le distanze tra i paesi considerando sia le reti culturali che le distribuzioni dei tratti culturali, attraverso il metodo DISTATIS, questo Capitolo osserva come l'aggiunta della componente di rete a quella distributiva classica, modifichi sostanzialmente la misura della distanza culturale sia nel caso di pochi tratti culturali (6, 10 e 14) che nel caso di pi\uf9 tratti culturali (60). Infine, esso afferma che la struttura di rete della cultura nazionale \ue8 importante per la definizione della distanza culturale tra i paesi del mondo e trova due misure finali di distanza: il Compromise_Large (da 60 variabili) e il Compromise_IW (dalle variabili della mappa culturale di Inglehart-Welzel). L'effetto delle variabili culturali sulla situazione economica di un paese, o pi\uf9 in generale di un'area geograficamente definita, \ue8 stato negli ultimi anni scandagliato dalla letteratura economica. Le distanze culturali, genetiche, geografiche, climatiche, semantiche, etniche, linguistiche, politiche sono state spesso incluse nei modelli econometrici come variabili indipendenti o di controllo. Il Capitolo 2 segue questa letteratura, prima confrontando individualmente tre misurazioni della distanza culturale calcolate nel Capitolo 1 con altre distanze usate in letteratura assieme alla distanza culturale o come proxy di essa, e poi confrontandole (le misure di distanza culturale e quelle dalla letteratura) congiuntamente tramite DISTATIS. Le tre distanze culturali sono le due nuove misure di cui sopra (Compromise_Large e Compromise_IW) e l'IW index ottenuto come distanza euclidea tra i paesi nella mappa culturale di Inglehart-Welzel, mentre le altre distanze prendono in considerazione la condizione climatica, l'etnia e la lingua, la genetica ed il recente fenomeno di Facebook. Infine, questo Capitolo considera tutte le misure di distanza all\u2019interno di un Social Relations Regression Model (SRRM) che stima la distanza tra i paesi in base al PIL pro capite (anno 2017). Il risultato finale mostra che le distanze culturali sono poco correlate con le distanze prese dalla letteratura, e quando si trova un compromesso tra di loro, di solito la Compromise_Large \ue8 caratterizzata da un peso leggermente superiore. La conclusione principale riguarda l'importante potere esplicativo della distanza Compromise_Large sulla distanza in PIL pro capite rispetto a quello della IW index e della Compromise_IW, la quale ha un significato intermedio tra le due. Ci\uf2 conferma l'importanza di considerare la rete culturale nazionale di interdipendenze tra tratti culturali nella definizione generale della distanza culturale, ed anche che l\u2019aggiunta di un numero maggiore di tratti culturali pu\uf2 influire nella sua specificazione, seppur i tratti culturali considerati da Ronald Inglehart e Christian Welzel nella costruzione della loro mappa culturale sembrano catturare gi\ue0 una buona parte dell\u2019informazione culturale dei paesi. La produzione abnorme di dati nel nostro tempo ha permesso l'osservazione di grandi collezioni di reti all\u2019interno di un campo di analisi specifico, le quali possono essere caratterizzate anche da una diversa dimensione l\u2019una dall\u2019altra (ad esempio si pu\uf2 pensare alla rete commerciale tra paesi di ogni prodotto). Una rete \ue8 un oggetto complesso, per cui un modo comune per analizzare e comparare congiuntamente un set di reti \ue8 ridurne la complessit\ue0 proiettandole in uno spazio ridotto attraverso i descrittori che le caratterizzano. \uc8 qui che sorge il problema analizzato nel Capitolo 3: qual \ue8 il sottoinsieme di descrittori che mantiene le caratteristiche delle reti il pi\uf9 possibile invariate nel processo di mapping, ovvero proietta in punti diversi dello spazio reti non isomorfe e raggruppa vicine reti strutturalmente simili tra di loro e lontano reti dissimili? Attraverso una simulazione di reti da quattro modelli generativi (Random, Scale-free, Small-world e Stochastic block model) e la selezione di un ampio insieme di descrittori riferenti ai livelli micro, meso e macro di analisi della rete, questo Capitolo trova tramite il metodo di Subgroup Discovery un piccolo sottoinsieme di descrittori. Questo sottoinsieme \ue8 composto da 5 descrittori: il momento primo del Coefficiente di Clustering Locale, 3 configurazioni di Motifs e il descrittore di Smallworldness. L'efficacia dei descrittori \ue8 valutata applicandoli all'insieme delle reti culturali binarie con 60 tratti culturali stimate nel Capitolo 1 e confrontando le distanze tra questi punti-rete nello spazio dei descrittori con distanze di reti popolari in letteratura. Le principali innovazioni sono due: la costruzione di un nuovo indice di distanza culturale tra i paesi, in cui \ue8 inclusa la rete culturale di interdipendenze tra tratti culturali; la selezione di un piccolo sottoinsieme efficiente di descrittori per la proiezione nello spazio di insiemi di reti binarie che possono avere grandezza diversa l\u2019una dall\u2019altra.In economic relations, in international agreements and in institutional dialogue, the word distance is one of the most enunciated. There are exogenous distances to be bridged to ignite a bond, sometimes there are necessary cracks and other times unavoidable breaks, but this may depend, as well as geographical and physical distances, and implicit interests, largely on the cultural status of groups of individuals. The quantitative evaluation of the distance between two entities is a dyadic property and as such, the presence, intensity, direction and sign of their tie is a way to undertake it. Since entities can be individuals, objects, companies, countries, planets, as well as networks referring to specific contexts, and the way to measure similarity between them is various, a peculiarity thing of distances is their changeable nature. While physical distances are almost objectively computable, in case of culture (and even other more or less broad concepts) using a method rather than another could radically change the proximity relationship between entities, especially if they have a high degree of complexity. The cultural background plays an important role in determining the socio-economic status of a country and its characterization in terms of similarity to other countries. The Chapter 1 - using data from the WVS/EVS Joint 2017 - operationalizes a definition of culture that takes into account the interdependencies between cultural traits at country level and calculates a new measure of cultural distance. Taking advantage of a recent Bayesian algorithm by Gaussian copula graphical model, this Chapter estimates for each of 76 countries included in the WVS/EVS Joint 2017, the cultural network of interdependencies between cultural traits considering different sets of them: the 6 from the first battery of questions, the 10 of the Inglehart-Welzel Cultural Map, the 14 of the Inglehart-Welzel Cultural Map, where for \u201cPost-materialism\u201d and \u201cAutonomy\u201d indices are used the variables from which they are derived, and 60 cultural traits of which, 14 as previously defined, 6 refer to the first battery of questions and the remaining 40 are selected to get a number that can cope with the trade-off between processing time and the minimum number of missing values per country. After defining the distances between countries considering both cultural networks and distributions of cultural traits, this Chapter observes via DISTATIS how the addition of the network component to the classic distributional one, substantially modifies the measure of cultural distance both in the case of a few cultural traits (6, 10 and 14) and in the case of more cultural traits (60). Finally, it affirms that the network structure of the national culture matters for the definition of the cultural distance among worldwide countries and finds two final distance measures: Compromise_Large (from 60 variables) and Compromise_IW (from the Inglehart-Welzel cultural map variables). The effect of cultural variables on the economic situation of a country or more generally of a geographically definable area, has been scoured in recent years by the economic literature. Cultural, genetic, geographical, climatic, semantic, ethnic, linguistic, political distances have often been included in econometric models as independent or control variables. The Chapter 2 follows this literature, firstly by individually comparing three measurements of cultural distance calculated in Chapter 1 with other distances used in literature together with cultural distance or as a proxy of it, and secondly by jointly comparing them (the measurements of cultural distance and those from literature) via DISTATIS. The three cultural distances are the two new measures mentioned above (Compromise_Large and Compromise_IW) and the IW index obtained as Euclidean distance between countries in the Inglehart-Welzel cultural map, while the other distances take into consideration climatic condition, ethnicity and language, genetics and the recent phenomenon of Facebook. Finally, this Chapter considers these distance measures into a Social Relations Regression Model (SRRM) which estimates the distance between countries in GDP per capita (year 2017). The final result shows that cultural distances are poorly correlated with the distances from the literature, and when a compromise is found between them, usually the Compromise_Large is characterized by a slightly higher weight. The main conclusion concerns the important explanatory power of the Compromise_Large distance on the distance in GDP per capita compared to that of the IW index and the Compromise_IW, which has an intermediate meaning between the two. This confirms the importance of considering the national cultural network of interdependencies between cultural traits in the overall definition of cultural distance, and also that the addition of more cultural traits may influence its specification, although the cultural traits considered by Inglehart and Welzel in the construction of their cultural map seem to capture already a good part of the cultural information of the countries. The abnormal production of data in our time has allowed the observation of large collections of networks within a specific field of analysis, which can also be characterized by a different size from each other, e.g. you can think of the trade network of each product between countries. A network is a complex object, so a common way to analyze and compare a set of networks is to reduce their complexity by mapping them into a space through the descriptors that characterize them. This is where the problem analyzed in Chapter 3 arises: what is the subset of descriptors that keeps the characteristics of networks as much as possible unchanged in the mapping process, namely projects non-isomorphic networks in different points of the space and groups nearby networks structurally similar and distant networks dissimilar? Through a simulation of networks from four generative models (Random, Scale-free, Small-world and Stochastic block model) and the selection of a wide set of descriptors of the micro, meso and macro-level of network analysis, this Chapter finds evidence of a small subset of descriptors via Subgroup Discovery. This subset is composed by 5 descriptors: the first moment of the Local Clustering Coefficient, 3 Motifs configurations and the descriptor of Smallworldness. The effectiveness of descriptors is evaluated by applying them to the set of binary cultural networks with 60 cultural traits estimated in Chapter 1 and comparing distances between these points-network in the space of the descriptors with popular network distances used in literature. Two are the main innovations: the construction of a new index of cultural distance among countries, in which is included the cultural network of interdependencies among cultural traits; the selection of a small efficient subset of descriptors for mapping in the space of sets of binary networks, which can also be characterized by a different size from each other