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Essays on the Network Analysis of Culture
Full text linkNelle relazioni economiche, negli accordi internazionali e nel dialogo istituzionale, la parola distanza è una delle più enunciate. Ci sono distanze esogene da colmare per creare legami, a volte ci sono chiusure necessarie e altre volte rotture inevitabili, ma questo può dipendere, così 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 entità è una proprietà diadica ed in quanto tale, la presenza, intensità, direzione e segno di un legame rappresenta un modo per catturarla. Poiché le entità possono essere individui, oggetti, società, paesi, pianeti, così come reti che si riferiscono a contesti specifici, e il modo di misurare la somiglianza tra di loro può essere vario, una cosa peculiare delle distanze è la loro natura mutevole. Mentre le distanze fisiche sono quasi oggettivamente calcolabili, nel caso della cultura (ed anche di altri concetti più o meno ampi) l’utilizzo di un metodo rispetto ad un altro potrebbe cambiare radicalmente la relazione di distanza tra le entità, soprattutto se esse hanno un alto grado di complessità.
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 “Post-materialism” e “Autonomy” 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’algoritmo 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 più tratti culturali (60). Infine, esso afferma che la struttura di rete della cultura nazionale è 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 più in generale di un'area geograficamente definita, è 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’interno 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 è 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. Ciò conferma l'importanza di considerare la rete culturale nazionale di interdipendenze tra tratti culturali nella definizione generale della distanza culturale, ed anche che l’aggiunta di un numero maggiore di tratti culturali può influire nella sua specificazione, seppur i tratti culturali considerati da Ronald Inglehart e Christian Welzel nella costruzione della loro mappa culturale sembrano catturare già una buona parte dell’informazione culturale dei paesi.
La produzione abnorme di dati nel nostro tempo ha permesso l'osservazione di grandi collezioni di reti all’interno di un campo di analisi specifico, le quali possono essere caratterizzate anche da una diversa dimensione l’una dall’altra (ad esempio si può pensare alla rete commerciale tra paesi di ogni prodotto). Una rete è un oggetto complesso, per cui un modo comune per analizzare e comparare congiuntamente un set di reti è ridurne la complessità proiettandole in uno spazio ridotto attraverso i descrittori che le caratterizzano. È qui che sorge il problema analizzato nel Capitolo 3: qual è il sottoinsieme di descrittori che mantiene le caratteristiche delle reti il più 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 è 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 è 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 è 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’una dall’altra.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 “Post-materialism” and “Autonomy” 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
Introducing Weighted Triad Census through Peeling Algorithm. An Application to Football Passing Networks
No full textCultures as networks of cultural traits: A unifying framework for measuring culture and cultural distances
Full text linkMaking use of the information from the World Value Survey (WVS), and
operationalizing a definition of national culture that encompasses both the
relevance of specific cultural traits and the interdependence among them, this
paper proposes a methodology to reveal the latent structure of national culture
and to measure cultural distance between countries that takes into account both
the difference in cultural traits and the difference in the network structure
of national cultures. Exploiting the possibilities offered by copula graphical
models for discrete data, this paper infers the cultural networks of all the
countries included in the WVS (Wave 6) and proposes a novel unifying framework
to measure national culture and international cultural distances. The Jeffreys'
divergence between copula graphical models, taken as the measure of cultural
distance between countries, captures the orthogonality of the two components of
cultural distance: the one based on cultural traits and the one based on the
network structure among them. Moreover, the two components are shown to
correlate with different national and structural characteristics of cultural
networks, thus encompassing the different informational sets related to
national cultures.Comment: 27 pages, 12 figures, 4 table
Community structure of the football transfer market network: the case of Italian Serie A
No full textThe men’s football transfer market represents a complex phenomenon requiring suitable methods for an in-depth study. Network Analysis may be employed to measure the key elements of the transfer market through network indicators, such as degree centrality, hub and authority scores, and betweenness centrality. Furthermore, community detection methods can be proposed to unveil unobservable patterns of the football market, even considering auxiliary variables such as the type of transfer, the age or the role of the player, and the agents involved in the transfer flow. These methodologies are applied to the flows of player transfers generated by the 20 teams of the Italian first division (Serie A). These flows include teams from all over the world. We consider the summer market session of 2019, at the beginning of the season 2019-2020. Results also help to better understand some peculiarities of the Italian football transfer market in terms of the different approaches of the elite teams. Network indices show the presence of different market strategies, highlighting the role of mid-level teams such as Atalanta, Genoa, and Sassuolo. The network reveals a core-periphery structure splitted into several communities. The Infomap algorithm identifies 14 single team-based communities and three communities formed by two teams. Two of the latter are composed of a top team and a mid-level team, suggesting the presence of collaboration and similar market behavior, while the third is guided by two teams promoted by the second division (Serie B)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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