Combination of spatial, temporal and social network data of geolocated Twitter messages. (a) Home and work locations of users are identified through the distribution of timestamps on all their collected tweets within their most frequently visited spatial clusters. We assign a possible home location (8 p.m.–8 a.m.) and a possible work location (9 a.m.–5 p.m.) to each user45,46 as their most frequently visited location in the given period. The histogram represents the timeline of tweets for the clusters of a sample user. (b) Commuting is defined as the overhead distance between users’ home and work locations. The colorbar of the map indicates the income level of census tracts. (c) Twitter ego network of a sample user based on mutual followership. The coloring of nodes also corresponds to the level of income in the home tract of users.
Millions commute to work every day in cities and interact with colleagues, partners, friends, and strangers. Commuting facilitates the mixing of people from distant and diverse neighborhoods, but whether this has an imprint on social inclusion or instead, connections remain assortative is less explored. In this paper, we aim to better understand income sorting in social networks inside cities and investigate how commuting distance conditions the online social ties of Twitter users in the 50 largest metropolitan areas of the United States. An above-median commuting distance in cities is linked to more diverse individual networks, moreover, we find that longer commutes are associated with a nearly uniform, moderate reduction of overall social tie assortativity across all cities. This suggests a universal relation between long-distance commutes and the integration of social networks. Our results inform policy that facilitating access across distant neighborhoods can advance the social inclusion of low-income groups.
Discrepancy and over/underrepresentation of broad research areas. Discrepancy is defined as the percentage of scientists who are among the top 25% based on their online success but do not belong to the top 25% according to h index. Discrepancy is shown per research area for the entire sample of scholars (gray circle), only men (green), and only women (yellow). Circle size indicates the number of scholars who had article mentions on Altmetric in each of those three groups. Over / underrepresentation of broad research areas on Altmetric is evaluated in comparison with WoS.
Unbiased science dissemination has the potential to alleviate some of the known gender disparities in academia by exposing female scholars’ work to other scientists and the public. And yet, we lack comprehensive understanding of the relationship between gender and science dissemination online. Our large-scale analyses, encompassing half a million scholars, revealed that female scholars’ work is mentioned less frequently than male scholars’ work in all research areas. When exploring the characteristics associated with online success, we found that the impact of prior work, social capital, and gendered tie formation in coauthorship networks are linked with online success for men, but not for women—even in the areas with the highest female representation. These results suggest that while men’s scientific impact and collaboration networks are associated with higher visibility online, there are no universally identifiable facets associated with success for women. Our comprehensive empirical evidence indicates that the gender gap in online science dissemination is coupled with a lack of understanding the characteristics that are linked with female scholars’ success, which might hinder efforts to close the gender gap in visibility.
(A) The maximum spanning tree of the collaboration network across NUTS 3 regions in Europe reveals the importance of national centers. (B) Most of the repeated collaborations remain within country borders, and strongest ties are concentrated within close proximity of innovative hubs. (C) The 7 communities of the collaboration network span across countries, with the exception of Germany that is divided into two communities and Italy, but are mostly concentrated in large regions. (D) Repeated collaboration is organized into 23 smaller-scale clusters
a Cumulative distribution of income in a relatively equal town (Ajka, gold) and a relatively unequal one (Gödöllő, dark blue). Sampled social networks in Gödöllő (b) and Ajka (c). Node colors represent network communities revealed by the Louvain method in both networks. d Income inequality measured by the Gini index (Gi,2011) for towns with more than 15,000 inhabitants (n = 91) correlates with the fragmentation (Fi) of their social networks (Pearson’s ρ = 0.44). Gold dot: Ajka (Gi,2011 = 0.43, Fi = 0.3); dark blue dot: Gödöllő (Gi,2011 = 0.54, Fi = 0.36); blue dots: all other towns. Fitted line represents a linear regression Gi,2011 = 0.36 + 0.37Fi with R2 = 0.198. The shade area depicts 95% confidence interval. The dashed horizontal line represents the mean of Gi,2011 = 0.488. e We plot the correlation between town Gini scores in 2011 and 2016 (Gi,2011 and Gi,2016). f The relationship between network fragmentation (Fi) and inequality in 2016 is stronger in those towns where initial inequality (Gi,2011) is high. β, the marginal effect of town social network fragmentation (Fi) on the Gini of the town in 2016 (Gi,2016), becomes significant around the mean of the Gini in 2011 (Gi,2011). It increases as Gi,2011 grows. Points depict estimated marginal effects at the mean and bars represent their 95% confidence intervals (n = 474).
Social networks amplify inequalities by fundamental mechanisms of social tie formation such as homophily and triadic closure. These forces sharpen social segregation, which is reflected in fragmented social network structure. Geographical impediments such as distance and physical or administrative boundaries also reinforce social segregation. Yet, less is known about the joint relationships between social network structure, urban geography, and inequality. In this paper we analyze an online social network and find that the fragmentation of social networks is significantly higher in towns in which residential neighborhoods are divided by physical barriers such as rivers and railroads. Towns in which neighborhoods are relatively distant from the center of town and amenities are spatially concentrated are also more socially segregated. Using a two-stage model, we show that these urban geography features have significant relationships with income inequality via social network fragmentation. In other words, the geographic features of a place can compound economic inequalities via social networks.
Geography of respondents’ national (a) and global (b) networks. Node size represents number of connections. Links tend to connect the region to major cities or countries, but geographic proximity also matters.
Social connections that reach distant places are advantageous for individuals, firms and cities, providing access to new skills and knowledge. However, systematic evidence on how firms build global knowledge access is still lacking. In this paper, we analyse how global work connections relate to differences in the skill composition of employees within companies and local industry clusters. We gather survey data from 10% of workers in a local industry in Sweden, and complement this with digital trace data to map co-worker networks and skill composition. This unique combination of data and features allows us to quantify global connections of employees and measure the degree of skill similarity and skill relatedness to co-workers. We find that workers with extensive local networks typically have skills related to those of others in the region and to those of their co-workers. Workers with more global ties typically bring in less related skills to the region. These results provide new insights into the composition of skills within knowledge-intensive firms by connecting the geography of network contacts to the diversity of skills accessible through them.
Adoption peak prediction on local scales with the Bass model. (A) The Bass DE model estimates on the monthly adoption trend and a smoothed empirical adoption trend (3-month moving average) are compared. (B) Times of adoption peaks vary across towns. (C) Estimated pi and qi result in same adoption peak with fixed pi, except in early adoption cases when qi is high. (D) Estimated peaks of adoption correlate with empirical peaks of adoption (p=0.74). (E) Prediction Error in town i. (F) Dots are point estimates of linear univariate regressions and bars depict standard errors. Dependent variable is scaled with its maximum value and independent variables are log-transformed with base 10.
The urban–rural divide is increasing in modern societies calling for geographical extensions of social influence modelling. Improved understanding of innovation diffusion across locations and through social connections can provide us with new insights into the spread of information, technological progress and economic development. In this work, we analyze the spatial adoption dynamics of iWiW, an Online Social Network (OSN) in Hungary and uncover empirical features about the spatial adoption in social networks. During its entire life cycle from 2002 to 2012, iWiW reached up to 300 million friendship ties of 3 million users. We find that the number of adopters as a function of town population follows a scaling law that reveals a strongly concentrated early adoption in large towns and a less concentrated late adoption. We also discover a strengthening distance decay of spread over the life-cycle indicating high fraction of distant diffusion in early stages but the dominance of local diffusion in late stages. The spreading process is modelled within the Bass diffusion framework that enables us to compare the differential equation version with an agent-based version of the model run on the empirical network. Although both model versions can capture the macro trend of adoption, they have limited capacity to describe the observed trends of urban scaling and distance decay. We find, however that incorporating adoption thresholds, defined by the fraction of social connections that adopt a technology before the individual adopts, improves the network model fit to the urban scaling of early adopters. Controlling for the threshold distribution enables us to eliminate the bias induced by local network structure on predicting local adoption peaks. Finally, we show that geographical features such as distance from the innovation origin and town size influence prediction of adoption peak at local scales in all model specifications.
(a) The density of all the possible technological combinations on a complexity‐complexity plot (1980–2010), (b) The density of all observed technological combinations on a complexity‐complexity plot (1980–2010).
Relatedness has become a key concept for studying the diversification of firms, regions and countries. However, studies tend to treat relatedness as being time‐invariant or, alternatively, consider its evolution as exogenously given. This study argues that relatedness is inherently dynamic and endogenous to technological and economic developments. Using patent data, we test the extent to which relatedness between technologies developed along co‐location and differences in technological complexity in 1980–2010. Our results show that co‐located technologies are more likely to become related over time. Moreover, our results suggest that co‐location and complexity of technologies are conducive to the intensification of relatedness over time.