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

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

Prioritizing high-contact professions raises effectiveness of vaccination campaigns

Recent studies have proposed network interventions for reducing the propagation of COVID-19. By restricting close range contact to occur only within predetermined interaction structures, the speed and reach of COVID-19 spread can theoretically be reduced. However, even severe social distancing policies such as full-scale lockdowns can only temporarily reduce infections and hospitalizations, leaving large-scale vaccination as the primary vehicle for sustainable control over the SARS-CoV-2 virus. Nonetheless, global vaccine roll-out has logistical and financial limits. The challenge is how to effectively control the virus with limited supplies. A twenty-year-old idea from network science is that vaccination campaigns would be much more effective if high contact individuals were preferentially targeted. Implementation is impeded by the ethical and practical problem of differentiating vaccine access on the basis of a personal characteristic that is informal and private. Here we develop an agent-based model on how to effectively vaccinate in times of a pandemic by prioritizing specific occupational groups. We draw on data from a survey conducted at the beginning of the COVID-19 pandemic in early 2020 that measures close-range contact for occupational groups. The data reveal substantial occupational differences, with teachers and cashiers being among the most connected and computer programmers among the least connected. To investigate whether this variability can produce significant gains when exploited in targeted vaccination programs, we first used a genetic algorithm to generate networks of 10,000 nodes that map the occupational contact data onto network degree. We then simulated epidemics and compared the effectivity of vaccination campaigns that target individuals either randomly or targeted by occupational group membership, prioritizing the highest reported average number of social contacts. Our simulations suggest that random distribution of vaccines amounts to 35% of nodes getting infected on average, compared to 60% in the baseline/no-vaccination condition. Prioritizing high contact professions, however, results in a mean of 20% of nodes getting infected, while the vast majority of epidemics are prevented entirely (median number of infections close to 0%). Furthermore, we show that the positive effect of targeted vaccination is stronger if networks are more clustered and if there is lower occupational group homophily. A comparison between random vaccination of 40% and targeted vaccination of 20% of the population (everything else equal) shows that the latter achieves similar numbers of cumulative infections with significantly later and lower epidemic peaks. Based on our findings, we propose that occupational groups can function as a reasonably effective proxy to increase effectiveness of vaccination campaigns

A symbolic data analysis approach to explore the relation between governance and performance in the Italian industrial districs

2013 - 2014Nowadays, complex phenomena need to bee analyzed through appropriate statistical methods that allow considering the knowledge hidden behind the classical data structure... [edited by author]XIII n.s

A multilevel Analysis of University attractiveness in the network flows from Bachelor to Master’s degree

In this work we aim to study the mobility choices of Italian students in the transition from bachelor to masters degree in order to assess the role played by the field of study. We consider micro-data from the Italian National Student Archive on a cohort of students enrolled for the first time at the university in a.y. 2011-12 who enrolled to a master degree program in the a.y. 2014-15 or 2015-16. We study the incoming and outgoing flows of students moving from bachelor to master’s degree between provinces and universities. We then assess the effects on mover choices of network centrality measures in terms of hub and authorities adopting a multilevel multinomial logit model

The use of network analysis to handle semantic differential data

The aim of this paper is to propose a method to transform semantic differential data into a network whose graph representation is interpreted as an empirical network of adjectives. The graph is constituted by the adjectives of the semantic differential task. Two adjectives are linked depending on the scoring assigned by a set of respondents. The proposed approach aims at using concepts and methods of Social Network Analysis to explore the network structure and study roles and positions of dominant adjectives. A simulation design has been realized to assess the stability of results under different conditions, i.e. in order to set the optimal threshold in presence of different data generator processes. A case study carried out on real data shows how the emerging network of adjectives can be effectively used to define the concept arising from a semantic differential task

An exploratory strategy for analyzing students’ mobility data.

Questo contributo analizza il tema della mobilita studentesca considerando la decisione degli studenti di trasferirsi nel passaggio dal primo al secondo anno di un corso di laurea triennale (churn risk). Da queste decisioni emergono diversi scenari di churn che dipendono dalla scelta di iscriversi in un’altra università, cambiare corso di laurea o entrambi. La Social Network Analysis permette di tracciare e visualizzare i legami tra le universita definiti in base a questo specifico tipo di mobilità. L’analisi ` e stata condotta considerando gli studenti iscritti alle università situate nella regione Campania (Italia). L’obiettivo e definire un indice dei flussi degli studenti basato sulle decisioni di trasferimento e la capacita di ritenzione delle università.his contribution deals with Italian students’ decision to churn in the tran sition from the first to the second year of a bachelor’s degree program. Based on these choices, three different churn scenarios are identified according to the deci sion to move towards a different university, to change degree programs, or both. Ex ploratory data analysis and Social Network methods are used to trace and visualize the links among universities defined considering these students’ flows. The analysis is conducted considering students enrolled in universities located in the Campania region (Italy). The aim is to define an inde

Discovering archetypal universities in higher education mobility flows in Italy

The aim of this contribution is to identify the archetypal universities in the Italian students’ mobility network in terms of their attitude in attracting students. We define a set of networks according to the disciplinary groups by relying upon administrative data regarding students’ mobility between bachelor’s and master’s degrees. For each disciplinary group, a network has been defined by considering the universities as nodes and the flows of students moving between nodes as links. Then, in each network, the set of archetypal universities is based on several network centrality indexes. Finally, these archetypes are used as benchmarks to identify the main determinants of universities’ performances

A Network-Based Indicator of Travelers Performativity on Instagram

The spread of Internet and online social media has created a huge amount of data able to provide new insights to researchers in dierent disciplinary elds, but it also presents new challenges for data science. Data arising from online social networks can be naturally coded as relational data in af- liation and adjacency matrices, then analyzed with social network analysis. In this study, we apply an interdisciplinary approach (based on automatic visual content analysis, social network analysis, and exploratory statistical techniques) to dene and derive a suitable indicator for characterizing places, along with the online activities of travelers, in terms of sharing images. We envisage a novel storytelling perspective where stories are related to places and the narrative activity is realized through posting images. Specically, we use data extracted from an online social network (i.e., Instagram) to identify travelers' paths among sites of interests. Starting from a large collection of pictures geolocalized in a pre-specied set of locations (i.e., ve locations in the Campania region of Italy during the 2018 Christmas season), we use automatic alternative text to produce an ex-post taxonomy of images on the most recurrent themes emerging from pictures posted on Instagram. Quantitative measures dened on the co-occurrence of locations and the emerging themes are used to build a statistical indicator able to characterize paths among dierent locations as narrated from travelers' posts. The proposed analysis, presented and discussed along with real data, can be useful for stakeholders interested in the elds of policy-making, communication design, and territory proling strategies