Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Di Falco; Salvatore, Feri, Francesco, Pin, Paolo First page: 2 Abstract: In this paper, we propose a network model to explain the implications of the pressure to share resources. Individuals use the network to establish social interactions that allow them to increase their income. They also use the network as a safety and to ask for assistance in case of need. The network is therefore a system characterized by social pressure to share and redistribute surplus of resources among members. The main result is that the potential redistributive pressure from other network members causes individuals to behave inefficiently. The number of social interactions used to employ workers displays a non-monotonic pattern with respect to the number of neighbors (degree): it increases for intermediate degree and decreases for high degree. Respect to a benchmark case without social pressure, individuals with few (many) network members interact more (less). Finally, we show that these predictions are consistent with the results obtained in a set of field experiments run in rural Tanzania. PubDate: 2025-01-09 DOI: 10.1017/nws.2024.18
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Fernandez; Guillaume P. First page: 3 Abstract: This study explores the relationship between alter centrality in various social domains and the perception of linguistic similarity within personal networks. Linguistic similarity perception is defined as the extent to which individuals perceive others to speak similarly to themselves. A survey of 126 college students and their social connections (n = 1035) from the French-speaking region of Switzerland was conducted. We applied logistic multilevel regressions to account for the hierarchical structure of dyadic ties. The results show that alters holding central positions in supportive networks are positively associated with perceived linguistic similarity, while those who are central in conflict networks show a negative association. The role of ambivalence yielded mixed results, with a positive and significant association emerging when ambivalence was linked to family members. PubDate: 2025-02-10 DOI: 10.1017/nws.2025.1
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Angst; Mario, Müller, Neitah Noemi, Walker, Viviane First page: 4 Abstract: Understanding and tracking societal discourse around essential governance challenges of our times is crucial. One possible heuristic is to conceptualize discourse as a network of actors and policy beliefs.Here, we present an exemplary and widely applicable automated approach to extract discourse networks from large volumes of media data, as a bipartite graph of organizations and beliefs connected by stance edges. Our approach leverages various natural language processing techniques, alongside qualitative content analysis. We combine named entity recognition, named entity linking, supervised text classification informed by close reading, and a novel stance detection procedure based on large language models.We demonstrate our approach in an empirical application tracing urban sustainable transport discourse networks in the Swiss urban area of Zürich over 12 years, based on more than one million paragraphs extracted from slightly less than two million newspaper articles.We test the internal validity of our approach. Based on evaluations against manually automated data, we find support for what we call the window validity hypothesis of automated discourse network data gathering. The internal validity of automated discourse network data gathering increases if inferences are combined over sliding time windows.Our results show that when leveraging data redundancy and stance inertia through windowed aggregation, automated methods can recover basic structure and higher-level structurally descriptive metrics of discourse networks well. Our results also demonstrate the necessity of creating high-quality test sets and close reading and that efforts invested in automation should be carefully considered. PubDate: 2025-04-02 DOI: 10.1017/nws.2025.4
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Aguech; Rafik, Mahmoud, Hosam, Mohamed, Hanene, Yang, Zhou First page: 5 Abstract: We introduce the exponentially preferential recursive tree and study some properties related to the degree profile of nodes in the tree. The definition of the tree involves a radix . In a tree of size (nodes), the nodes are labeled with the numbers . The node labeled attracts the future entrant with probability proportional to .We dedicate an early section for algorithms to generate and visualize the trees in different regimes. We study the asymptotic distribution of the outdegree of node , as , and find three regimes according to whether (subcritical regime), (critical regime), or (supercritical regime). Within any regime, there are also phases depending on a delicate interplay between and PubDate: 2025-04-14 DOI: 10.1017/nws.2025.3
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Sischka; Benjamin, Kauermann, Göran First page: 6 Abstract: This paper focuses on the comparison of networks on the basis of statistical inference. For that purpose, we rely on smooth graphon models as a nonparametric modeling strategy that is able to capture complex structural patterns. The graphon itself can be viewed more broadly as local density or intensity function on networks, making the model a natural choice for comparison purposes. More precisely, to gain information about the (dis-)similarity between networks, we extend graphon estimation towards modeling multiple networks simultaneously. In particular, fitting a single model implies aligning different networks with respect to the same graphon estimate. To do so, we employ an EM-type algorithm. Drawing on this network alignment consequently allows a comparison of the edge density at local level. Based on that, we construct a chi-squared-type test on equivalence of network structures. Simulation studies and real-world examples support the applicability of our network comparison strategy. PubDate: 2025-05-15 DOI: 10.1017/nws.2025.5
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Mepham; Kieran, Vörös, András, Stadtfeld, Christoph First page: 7 Abstract: Ideological and relational polarization are two increasingly salient political divisions in Western societies. We integrate the study of these phenomena by describing society as a multilevel network of social ties between people and attitudinal ties between people and political topics. We then define and propose a set of metrics to measure ‘network polarization’: the extent to which a community is ideologically and socially divided. Using longitudinal network modelling, we examine whether observed levels of network polarization can be explained by three processes: social selection, social influence, and latent-cause reinforcement. Applied to new longitudinal friendship and political attitude network data from two Swiss university cohorts, our metrics show mild polarization. The models explain this outcome and suggest that friendships and political attitudes are reciprocally formed and sustained. We find robust evidence for friend selection based on attitude similarity and weaker evidence for social influence. The results further point to latent-cause reinforcement processes: (dis)similar attitudes are more likely to be formed or maintained between individuals whose attitudes are already (dis)similar on a range of political issues. Applied across different cultural and political contexts, our approach may help to understand the degree and mechanisms of divisions in society. PubDate: 2025-05-21 DOI: 10.1017/nws.2025.2
Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Authors:Będowska-Sójka; Barbara, Wójcik, Piotr, Giordano, Sabrina First page: 1 Abstract: This study aims to explore the dependencies on the cryptocurrency market using social network tools. We focus on the correlations observed in the cryptocurrency returns. Based on the sample of cryptocurrencies listed between January 2015 and December 2022 we examine which cryptos are central to the overall market and how often major players change. Static network analysis based on the whole sample shows that the network consists of several communities strongly connected and central, as well as a few that are disconnected and peripheral. Such a structure of the network implies high systemic risk. The day-by-day snapshots show that the network evolves rapidly. We construct the ranking of major cryptos based on centrality measures utilizing the TOPSIS method. We find that when single measures are considered, Bitcoin seems to have lost its first-mover advantage in late 2016. However, in the overall ranking, it still appears among the top positions. The collapse of any of the cryptocurrencies from the top of the rankings poses a serious threat to the entire market. PubDate: 2024-11-07 DOI: 10.1017/nws.2024.17
Hybrid journal (It can contain Open Access articles)
. In a tree of size 
(nodes), the nodes are labeled with the numbers 
. The node labeled 
attracts the future entrant 
with probability proportional to 
.We dedicate an early section for algorithms to generate and visualize the trees in different regimes. We study the asymptotic distribution of the outdegree of node 
, as 
, and find three regimes according to whether 
(subcritical regime), 
(critical regime), or 
(supercritical regime). Within any regime, there are also phases depending on a delicate interplay between 
and
