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Social networks are shaped by complex, intersecting identities that drive our connection preferences. These preferences weave networks where certain groups hold privileged positions, while others become marginalized. While previous research has examined the impact of single-dimensional identities on inequalities of social capital, social disparities accumulate nonlinearly, further harming individuals at the intersection of multiple disadvantaged groups. However, how multidimensional connection preferences affect network dynamics and in what forms they amplify or attenuate inequalities remains unclear. In this work, we systematically analyze the impact of multidimensionality on social capital inequalities through the lens of intersectionality. To this end, we operationalize several notions of intersectional inequality in networks. Using a network model, we reveal how attribute correlation (or consolidation) combined with biased multidimensional preferences lead to the emergence of counterintuitive patterns of inequality that are unobservable in one-dimensional systems. We calibrate the model with real-world high school friendship data and derive analytical closed-form expressions for the predicted inequalities, finding that the model's predictions match the observed data with remarkable accuracy. These findings hold significant implications for addressing social disparities and inform strategies for creating more equitable networks., Comment: 29 pages, 14 figures

We examine gender differences in collaboration networks and academic career progression in physics. We use the likelihood and time to become a principal investigator (PI) and the length of an author's career to measure career progression. Utilising logistic regression and accelerated failure time models, we examine whether the effect of collaboration behaviour varies by gender. We find that, controlling for the number of publications, the relationship between collaborative behaviour and career progression is almost the same for men and women. Specifically, we find that those who eventually reach principal investigator (PI) status, tend to have published with more unique collaborators. In contrast, publishing repeatedly with the same highly interconnected collaborators and/or larger number of coauthors per publication is characteristic of shorter career lengths and those that do not attain PI status. We observe that the tie strength is stronger for women than men, and women tend to collaborate in more tightly connected and larger groups than men. Finally, we observe that women are less likely to attain the status of PI throughout their careers and have a lower survival probability compared to men, which calls for policies to close this crucial gap.

Recent research has challenged the widespread belief that gender inequities in academia would disappear simply by increasing the number of women. More complex causes might be at play, embodied in the networked structure of scientific collaborations. Here, we aim to understand the structural inequality between male and female scholars in the dissemination of scientific knowledge. We use a large-scale dataset of academic publications from the American Physical Society (APS) to build a time-varying network of collaborations from 1970 to 2020. We model knowledge dissemination as a contagion process in which scientists become informed based on the propagation of knowledge through their collaborators. We quantify the fairness of the system in terms of how women acquire and diffuse knowledge compared to men. Our results indicate that knowledge acquisition and diffusion are slower for women than expected. We find that the main determinant of women's disadvantage is the gap in the cumulative number of collaborators, highlighting how time creates structural disadvantages that contribute to marginalize women in physics. Our work sheds light on how the dynamics of scientific collaborations shape gender disparities in knowledge dissemination and calls for a deeper understanding on how to intervene to improve fairness and diversity in the scientific community.

Science is a collaborative endeavor in which "who collaborates with whom" profoundly influences scientists' career trajectories and success. Despite its relevance, little is known about how scholars facilitate new collaborations among their peers. In this study, we quantify brokerage in academia and study its effect on the careers of physicists worldwide. We find that early-career participation in brokerage increases later-stage involvement for all researchers, with increasing participation rates and greater career impact among more successful scientists. This cumulative advantage process suggests that brokerage contributes to the unequal distribution of success in academia. Surprisingly, this affects both women and men equally, despite women being more junior in all brokerage roles and lagging behind men's participation due to their late and slow arrival to physics. Because of its cumulative nature, promoting brokerage opportunities to early career scientists might help reduce the inequalities in academic success.

Our multidimensional identities determine how we interact with each other, shaping social networks through group-based connection preferences. While interactions along single dimensions have been extensively studied, the dynamics driving multidimensional connection preferences remain largely unexplored. In this work, we develop a network model of multidimensional social interactions to tackle two crucial questions: What is the structure of our latent connection preferences, and how do we integrate information from our multidimensional identities to connect with others? To answer these questions, we systematically model different latent preference structures and preference aggregation mechanisms. Then, we compare them using Bayesian model selection by fitting empirical data from high school friendship networks. We find that a simple latent preference model consistently outperforms more complex alternatives. The calibrated model provides robust measures of latent connection preferences in real-world networks, bringing insights into how one- and multidimensional groups interact. Finally, we develop natural operationalizations of dimension salience, revealing which aspects of identity are most relevant for individuals when forming connections.

The most fundamental social interactions among humans occur face to face. Their features have been extensively studied in recent years, owing to the availability of high-resolution data on individuals' proximity. Mathematical models based on mobile agents have been crucial to understand the spatio-temporal organization of face-to-face interactions. However, these models focus on dyadic relationships only, failing to characterize interactions in larger groups of individuals. Here, we propose a model in which agents interact with each other by forming groups of different sizes. Each group has a degree of social attractiveness, based on which neighboring agents decide whether to join. Our framework reproduces different properties of groups in face-to-face interactions, including their distribution, the correlation in their number, and their persistence in time, which cannot be replicated by dyadic models. Furthermore, it captures homophilic patterns at the level of higher-order interactions, going beyond standard pairwise approaches. Our work sheds light on the higher-order mechanisms at the heart of human face-to-face interactions, paving the way for further investigation of how group dynamics at a microscopic scale affects social phenomena at a macroscopic scale.

Diversity in leadership positions, including corporate boards, is an important aspect of equality. It is important because it is the key to better decision-making and innovation, and above all, it paves the way for future generations to participate and shape our society. Many studies emphasize the importance of the visibility of role models and the effect that connectivity has on the success of minorities in leadership. However, the connectivity of firms, the dynamics of the adoption of minorities into leadership positions, and the long-term effects in terms of group dynamics and visibility are not well understood. Here, we present a model that shows how these effects work together in a dynamic model that is calibrated with empirical data of firm and board networks. We show that homophily -- the appointment of minorities is influenced by the presence of minorities in a board and its neighboring entities -- is an important effect shaping the trajectory towards equality. We further show how perception biases and feedback related to the visibility of minority members influence the dynamic. We find that reaching equality can be sped up or slowed down depending on the distribution of minorities in central firms. These insights bear significant implications for policy-making geared towards fostering equality and diversity within corporate boards.

Unveiling individuals' preferences for connecting with similar others (choice homophily) beyond the structural factors determining the pool of opportunities, is a challenging task. Here, we introduce a robust methodology for quantifying and inferring choice homophily in a variety of social networks. Our approach employs statistical network ensembles to estimate and standardize homophily measurements. We control for group size imbalances and activity disparities by counting the number of possible network configurations with a given number of inter-group links using combinatorics. This method provides a principled measure of connection preferences and their confidence intervals. Our framework is versatile, suitable for undirected and directed networks, and applicable in scenarios involving multiple groups. To validate our inference method, we test it on synthetic networks and show that it outperforms traditional metrics. Our approach accurately captures the generative homophily used to build the networks, even when we include additional tie-formation mechanisms, such as preferential attachment and triadic closure. Results show that while triadic closure has some influence on the inference, its impact is small in homophilic networks. On the other hand, preferential attachment does not perturb the results of the inference method. We apply our method to real-world networks, demonstrating its effectiveness in unveiling underlying gender homophily. Our method aligns with traditional metrics in networks with balanced populations, but we obtain different results when the group sizes or degrees are imbalanced. This finding highlights the importance of considering structural factors when measuring choice homophily in social networks.

Migration's impact spans various social dimensions, including demography, sustainability, politics, economy and gender disparities. Yet, the decision-making process behind migrants choosing their destination remains elusive. Existing models primarily rely on population size and travel distance to explain flow fluctuations, overlooking significant population heterogeneities. Paradoxically, migrants often travel long distances and to smaller destinations if their diaspora is present in those locations. To address this gap, we propose the diaspora model of migration, incorporating intensity (the number of people moving to a country) and assortativity (the destination within the country). Our model considers only the existing diaspora sizes in the destination country, influencing the probability of migrants selecting a specific residence. Despite its simplicity, our model accurately reproduces the observed stable flow and distribution of migration in Austria (postal code level) and US metropolitan areas, yielding precise estimates of migrant inflow at various geographic scales. Given the increase in international migrations due to recent natural and societal crises, this study enlightens our understanding of migration flow heterogeneities, helping design more inclusive, integrated cities., Comment: 19 pages, 9 figures

Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performance, and epidemic spread. Today, these and many more issues can be studied at global scale thanks to the digital footprints that we generate when browsing the Web or using social media platforms. Unfortunately, scientists often struggle to access to such data primarily because it is proprietary, and even when it is shared with privacy guarantees, such data is either no representative or too big. In this tutorial, we will discuss recent advances and future directions in network modeling. In particular, we focus on how to exploit synthetic networks to study real-world problems such as data privacy, spreading dynamics, algorithmic bias, and ranking inequalities. We start by reviewing different types of generative models for social networks including node-attributed and scale-free networks. Then, we showcase how to perform a network selection analysis to characterize the mechanisms of edge formation of any given real-world network., Comment: Presented at The ACM Web Conference 2023

Core-periphery is a key feature of large-scale networks underlying a wide range of social, biological, and transportation phenomena. Despite its prevalence in empirical data, it is unclear whether this property is a consequence of more fundamental network evolution processes. While preferential attachment can create degree heterogeneity indistinguishable from core-periphery, it doesn't explain why specific groups of nodes gain dominance and become cores. We show that even small amounts of assortative attachment, e.g. homophily in social networks, can break this symmetry, and that the interplay of the two mechanisms leads to one of the groups emerging as a prominent core. A systematic analysis of the phase space of the proposed model reveals the levels of assortative and preferential attachment necessary for a group to become either core or periphery, depending on initial conditions. We find that relative group size is significant, with minority groups typically having a disadvantage on becoming the core for similar assortative attachment levels among groups. We also find that growing networks are less prone to develop core-periphery than dynamically evolving networks, and that these two network evolution mechanisms lead to different types of core-periphery structures. Analyzing five empirical networks, our findings suggest that core nodes are highly assortative, illustrating the potential of our model as a tool for designing and analyzing interventions on evolving networks., Comment: 5 figures

Homophily and heterophobia, the tendency for people with similar characteristics to preferentially interact with (or avoid) each other are pervasive in human social networks. Here, we develop an extension of the mathematical theory of urban scaling which describes the effects of homophily and heterophobia on social interactions and resulting economic outputs of cities. Empirical tests of our model show that increased residential racial heterophobia and segregation in U.S. cities are associated with reduced economic outputs and that the strength of this relationship increased throughout the 2010s. Our findings provide the means for the formal incorporation of general homophilic and heterophobic effects into theories of modern urban science and suggest that racial segregation is increasingly and adversely impacting the economic performance and connectivity of urban societies in the U.S.

Nominal assortativity (or discrete assortativity) is widely used to characterize group mixing patterns and homophily in networks, enabling researchers to analyze how groups interact with one another. Here we demonstrate that the measure presents severe shortcomings when applied to networks with unequal group sizes and asymmetric mixing. We characterize these shortcomings analytically and use synthetic and empirical networks to show that nominal assortativity fails to account for group imbalance and asymmetric group interactions, thereby producing an inaccurate characterization of mixing patterns. We propose adjusted nominal assortativity and show that this adjustment recovers the expected assortativity in networks with various level of mixing. Furthermore, we propose an analytical method to assess asymmetric mixing by estimating the tendency of inter- and intra-group connectivities. Finally, we discuss how this approach enables uncovering hidden mixing patterns in real-world networks., Comment: 19 pages, 3 figures

Improving the position of minorities in networks via interventions is a challenge of high theoretical and societal importance. In this work, we examine how different network growth interventions impact the position of minority nodes in degree rankings over time. We distinguish between two kinds of interventions: (i) group size interventions, such as introducing quotas, that regulate the ratio of incoming minority and majority nodes; and (ii) behavioural interventions, such as homophily, i.e. varying how groups interact and connect to each other. We find that even extreme group size interventions do not have a strong effect on the position of minorities in rankings if certain behavioural changes do not manifest at the same time. For example, minority representation in rankings is not increased by high quotas if the actors in the network do not adopt homophilic behaviour. As a result, a key finding of our research is that in order for the visibility of minorities to improve, group size and behavioural interventions need to be coordinated. Moreover, their potential benefit is highly dependent on pre-intervention conditions in social networks. In a real-world case study, we explore the effectiveness of interventions to reach gender parity in academia. Our work lays a theoretical and computational foundation for further studies aiming to explore the effectiveness of interventions in growing networks.

In this chapter, we provide an overview of recent advances in data-driven and theory-informed complex models of social networks and their potential in understanding societal inequalities and marginalization. We focus on inequalities arising from networks and network-based algorithms and how they affect minorities. In particular, we examine how homophily and mixing biases shape large and small social networks, influence perception of minorities, and affect collaboration patterns. We also discuss dynamical processes on and of networks and the formation of norms and health inequalities. Additionally, we argue that network modeling is paramount for unveiling the effect of ranking and social recommendation algorithms on the visibility of minorities. Finally, we highlight the key challenges and future opportunities in this emerging research topic., Comment: 11 pages, 1 figure, book chapter

Network-based people recommendation algorithms are widely employed on the Web to suggest new connections in social media or professional platforms. While such recommendations bring people together, the feedback loop between the algorithms and the changes in network structure may exacerbate social biases. These biases include rich-get-richer effects, filter bubbles, and polarization. However, social networks are diverse complex systems and recommendations may affect them differently, depending on their structural properties. In this work, we explore five people recommendation algorithms by systematically applying them over time to different synthetic networks. In particular, we measure to what extent these recommendations change the structure of bi-populated networks and show how these changes affect the minority group. Our systematic experimentation helps to better understand when link recommendation algorithms are beneficial or harmful to minority groups in social networks. In particular, our findings suggest that, while all algorithms tend to close triangles and increase cohesion, all algorithms except Node2Vec are prone to favor and suggest nodes with high in-degree. Furthermore, we found that, especially when both classes are heterophilic, recommendation algorithms can reduce the visibility of minorities., Comment: 11 pages, accepted at the WebSci'22 conference

During the COVID-19 pandemic, we witnessed a disproportionate infection rate among marginalized and low-income groups. Despite empirical evidence suggesting that structural inequalities in society contribute to health disparities, there has been little attempt to offer a computational and theoretical explanation to establish its plausibility and quantitative impact. This paper combines an agent-based network model and a compartmental susceptible-infectious-removed model (SIR) to explain how socioeconomic inequalities affect the dynamics of disease spreading. Specifically, we focus on two aspects of structural inequalities: wealth inequality and social segregation. Our computational model demonstrates that under high income inequality, the infection gap widens between the low-income and high-income groups, and also the overall infected cases increase. We also observed that social segregation between different socioeconomic status (SES) groups intensifies the spreading and hence mortality rates. Furthermore, we explain the second peak of the infectious cases during a pandemic as a result of a false sense of safety and loosening the quarantine, among the higher SES individuals. These findings send a strong message to policymakers; confinement measures must be accompanied by substantial financial assistance to those from lower-income groups so that the people regardless of their socioeconomic status can afford to stay in. Without financial assistance, lower-income individuals will remain in circulation, which will prolong the duration and magnitude of the infection.

International mobility in academia can enhance the human and social capital of researchers and consequently their scientific outcome. However, there is still a very limited understanding of the different mobility patterns among scholars with various socio-demographic characteristics. The aim of this study is twofold. First, we investigate to what extent individual factors associate with the mobility of researchers. Second, we explore the relationship between mobility and scientific activity and impact. For this purpose, we used a bibliometric approach to track the mobility of authors. To compare the scientific outcomes of researchers, we considered the number of publications and received citations as indicators, as well as the number of unique co-authors in all their publications. We also analysed the co-authorship network of researchers and compared centrality measures of mobile and non-mobile researchers. Results show that researchers from North America and Sub-Saharan Africa, particularly female ones, have the lowest, respectively, highest tendency towards international mobility. Having international co-authors increases the probability of international movement. Our findings uncover gender inequality in international mobility across scientific fields and countries. Across genders, researchers in the Physical sciences have the most and in the Social sciences the least rate of mobility. We observed more mobility for Social scientists at the advanced career stage, while researchers in other fields prefer to move at earlier career stages. Also, we found a positive correlation between mobility and scientific outcomes, but no apparent difference between females and males. Comparing the centrality of mobile and non-mobile researchers in the co-authorship networks reveals a higher social capital advantage for mobile researchers., Comment: 27 pages

Mounting evidence suggests that publications and citations of scholars in the STEM fields (Science, Technology, Engineering and Mathematics) suffer from gender biases. In this paper, we study the physics community, a core STEM field in which women are still largely underrepresented and where these gender disparities persist. To reveal such inequalities, we compare the citations received by papers led by men and women that cover the same topics in a comparable way. To do that, we devise a robust statistical measure of similarity between publications that enables us to detect pairs of similar papers. Our findings indicate that although papers written by women tend to have lower visibility in the citation network, pairs of similar papers written by men and women receive comparable attention when corrected for the time of publication. These analyses suggest that gender disparity is closely related to the first-mover and cumulative advantage that men have in physics, and is not an intentional act of discrimination towards women., Comment: 21 pages, 8 tables, 10 figures

Though algorithms promise many benefits including efficiency, objectivity and accuracy, they may also introduce or amplify biases. Here we study two well-known algorithms, namely PageRank and Who-to-Follow (WTF), and show to what extent their ranks produce inequality and inequity when applied to directed social networks. To this end, we propose a directed network model with preferential attachment and homophily (DPAH) and demonstrate the influence of network structure on the rank distributions of these algorithms. Our main findings suggest that (i) inequality is positively correlated with inequity, (ii) inequality is driven by the interplay between preferential attachment, homophily, node activity and edge density, and (iii) inequity is driven by the interplay between homophily and minority size. In particular, these two algorithms reduce, replicate and amplify the representation of minorities in top ranks when majorities are homophilic, neutral and heterophilic, respectively. Moreover, when this representation is reduced, minorities may improve their visibility in the rank by connecting strategically in the network. For instance, by increasing their out-degree or homophily when majorities are also homophilic. These findings shed light on the social and algorithmic mechanisms that hinder equality and equity in network-based ranking and recommendation algorithms., Comment: 23 pages, 7 figures and 3 tables in main manuscript. Includes supplementary material

Uncovering how inequality emerges from human interaction is imperative for just societies. Here we show that the way social groups interact in face-to-face situations can enable the emergence of disparities in the visibility of social groups. These disparities translate into members of specific social groups having fewer social ties than the average (i.e., degree inequality). We characterize group degree inequality in sensor-based data sets and present a mechanism that explains these disparities as the result of group mixing and group-size imbalance. We investigate how group sizes affect this inequality, thereby uncovering the critical size and mixing conditions in that a critical minority group emerges. If a minority group is larger than this critical size, it can be a well-connected, cohesive group; if it is smaller, minority cohesion widens degree inequality. Finally, we expose the under-representation of individuals in degree rankings due to mixing dynamics and propose a way to reduce such biases., Comment: 27 pages; 5 figures

Threshold models of cascades in the social sciences and economics explain the spread of opinion and innovation due to social influence. In threshold cascade models, fads or innovations spread between agents as determined by their interactions with other agents and their personal threshold of resistance. Typically, these models do not account for structure in the timing of interaction between the units. In this work, we extend a model of social cascades by Duncan Watts to temporal interaction networks. In our model, we assume friends and acquaintances influence agents for a certain time into the future. That is the influence of the past ages and becomes unimportant. Thus, our modified cascade model has an effective time window of influence. We explore two types of thresholds -- thresholds to fractions of the neighbors or absolute numbers. We try our model on six empirical datasets and compare them with null models., Comment: Chapter in Holme, Saram\"aki, eds., Temporal Networks (Springer, Berlin, 2013)

AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multi-disciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful Machine Learning (ML) algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features like race, sex, etc., Comment: 19 pages, 1 figure

Social norms can facilitate societal coexistence in groups by providing an implicitly shared set of expectations and behavioral guidelines. However, different social groups can hold different norms, and lacking an overarching normative consensus can lead to conflict within and between groups. In this paper, we present an agent-based model that simulates the adoption of norms in two interacting groups. We explore this phenomenon while varying relative group sizes and homophily/heterophily (two features of network structure), and initial group norm distributions. Agents update their norm according to an adapted version of Granovetter's threshold model, using a uniform distribution of thresholds. We study the impact of network structure and initial norm distributions on the process of achieving normative consensus and the resulting potential for intragroup and intergroup conflict. Our results show that norm change is most likely when norms are strongly tied to group membership. Groups end up with the most similar norm distributions when networks are heterophilic, with small to middling minority groups. High homophilic networks show high potential intergroup conflict and low potential intragroup conflict, while the opposite pattern emerges for high heterophilic networks., Comment: 29 pages, 5 figures

Emergent patterns of collective attention towards scientists and their research may function as a proxy for scientific impact which traditionally is assessed via committees that award prizes to scientists. Therefore it is crucial to understand the relationships between scientific impact and online demand and supply for information about scientists and their work. In this paper, we compare the temporal pattern of information supply (article creations) and information demand (article views) on Wikipedia for two groups of scientists: scientists who received one of the most prestigious awards in their field and influential scientists from the same field who did not receive an award. Our research highlights that awards function as external shocks which increase supply and demand for information about scientists, but hardly affect information supply and demand for their research topics. Further, we find interesting differences in the temporal ordering of information supply between the two groups: (i) award-winners have a higher probability that interest in them precedes interest in their work; (ii) for award winners interest in articles about them and their work is temporally more clustered than for non-awarded scientists., Comment: Accepted at the 2018 ACM on Web Science Conference, Amsterdam, Netherlands, May 27-30, 2018

We propose a new preferential attachment-based network growth model in order to explain two properties of growing networks: (1) the power-law growth of node degrees and (2) the decay of node relevance. In preferential attachment models, the ability of a node to acquire links is affected by its degree, its fitness, as well as its relevance which typically decays over time. After a review of existing models, we argue that they cannot explain the above-mentioned two properties (1) and (2) at the same time. We have found that apart from being empirically observed in many systems, the exponential growth of the network size over time is the key to sustain the power-law growth of node degrees when node relevance decays. We therefore make a clear distinction between the event time and the physical time in our model, and show that under the assumption that the relevance of a node decays with its age $\tau$, there exists an analytical solution of the decay function $f_R$ with the form $f_R(\tau) = \tau^{-1}$. Other properties of real networks such as power-law alike degree distributions can still be preserved, as supported by our experiments. This makes our model useful in explaining and analysing many real systems such as citation networks., Comment: 9 pages, 7 figures, ACM WebSci 2018

In this paper, we analyze a major part of the research output of the Networked Knowledge Organization Systems (NKOS) community in the period 2000 to 2016 from a network analytical perspective. We focus on the papers presented at the European and U.S. NKOS workshops and in addition four special issues on NKOS in the last 16 years. For this purpose, we have generated an open dataset, the "NKOS bibliography" which covers the bibliographic information of the research output. We analyze the co-authorship network of this community which results in 123 papers with a sum of 256 distinct authors. We use standard network analytic measures such as degree, betweenness and closeness centrality to describe the co-authorship network of the NKOS dataset. First, we investigate global properties of the network over time. Second, we analyze the centrality of the authors in the NKOS network. Lastly, we investigate gender differences in collaboration behavior in this community. Our results show that apart from differences in centrality measures of the scholars, they have higher tendency to collaborate with those in the same institution or the same geographic proximity. We also find that homophily is higher among women in this community. Apart from small differences in closeness and clustering among men and women, we do not find any significant dissimilarities with respect to other centralities., Comment: 14 pages, 6 figures, accepted paper in International Journal on Digital Libraries (IJDL)

Relational inference leverages relationships between entities and links in a network to infer information about the network from a small sample. This method is often used when global information about the network is not available or difficult to obtain. However, how reliable is inference from a small labelled sample? How should the network be sampled, and what effect does it have on inference error? How does the structure of the network impact the sampling strategy? We address these questions by systematically examining how network sampling strategy and sample size affect accuracy of relational inference in networks. To this end, we generate a family of synthetic networks where nodes have a binary attribute and a tunable level of homophily. As expected, we find that in heterophilic networks, we can obtain good accuracy when only small samples of the network are initially labelled, regardless of the sampling strategy. Surprisingly, this is not the case for homophilic networks, and sampling strategies that work well in heterophilic networks lead to large inference errors. These findings suggest that the impact of network structure on relational classification is more complex than previously thought., Comment: Accepted at the International workshop on Mining Attributed Networks (MATNet) workshop at WWW2018

People's perceptions about the size of minority groups in social networks can be biased, often showing systematic over- or underestimation. These social perception biases are often attributed to biased cognitive or motivational processes. Here we show that both over- and underestimation of the size of a minority group can emerge solely from structural properties of social networks. Using a generative network model, we show analytically that these biases depend on the level of homophily and its asymmetric nature, as well as on the size of the minority group. Our model predictions correspond well with empirical data from a cross-cultural survey and with numerical calculations on six real-world networks. We also show under what circumstances individuals can reduce their biases by relying on perceptions of their neighbors. This work advances our understanding of the impact of network structure on social perception biases and offers a quantitative approach for addressing related issues in society., Comment: 22 pages, 5 main figures, 1 table

Scientific collaborations shape ideas as well as innovations and are both the substrate for, and the outcome of, academic careers. Recent studies show that gender inequality is still present in many scientific practices ranging from hiring to peer-review processes and grant applications. In this work, we investigate gender-specific differences in collaboration patterns of more than one million computer scientists over the course of 47 years. We explore how these patterns change over years and career ages and how they impact scientific success. Our results highlight that successful male and female scientists reveal the same collaboration patterns: compared to scientists in the same career age, they tend to collaborate with more colleagues than other scientists, seek innovations as brokers and establish longer-lasting and more repetitive collaborations. However, women are on average less likely to adapt the collaboration patterns that are related with success, more likely to embed into ego networks devoid of structural holes, and they exhibit stronger gender homophily as well as a consistently higher dropout rate than men in all career ages.

Sampling from large networks represents a fundamental challenge for social network research. In this paper, we explore the sensitivity of different sampling techniques (node sampling, edge sampling, random walk sampling, and snowball sampling) on social networks with attributes. We consider the special case of networks (i) where we have one attribute with two values (e.g., male and female in the case of gender), (ii) where the size of the two groups is unequal (e.g., a male majority and a female minority), and (iii) where nodes with the same or different attribute value attract or repel each other (i.e., homophilic or heterophilic behavior). We evaluate the different sampling techniques with respect to conserving the position of nodes and the visibility of groups in such networks. Experiments are conducted both on synthetic and empirical social networks. Our results provide evidence that different network sampling techniques are highly sensitive with regard to capturing the expected centrality of nodes, and that their accuracy depends on relative group size differences and on the level of homophily that can be observed in the network. We conclude that uninformed sampling from social networks with attributes thus can significantly impair the ability of researchers to draw valid conclusions about the centrality of nodes and the visibility or invisibility of groups in social networks., Comment: Published at WWW'17

Homophily can put minority groups at a disadvantage by restricting their ability to establish links with people from a majority group. This can limit the overall visibility of minorities in the network. Building on a Barab\'{a}si-Albert model variation with groups and homophily, we show how the visibility of minority groups in social networks is a function of (i) their relative group size and (ii) the presence or absence of homophilic behavior. We provide an analytical solution for this problem and demonstrate the existence of asymmetric behavior. Finally, we study the visibility of minority groups in examples of real-world social networks: sexual contacts, scientific collaboration, and scientific citation. Our work presents a foundation for assessing the visibility of minority groups in social networks in which homophilic or heterophilic behaviour is present., Comment: 11 pages, 8 figures, under review

Computational social scientists often harness the Web as a "societal observatory" where data about human social behavior is collected. This data enables novel investigations of psychological, anthropological and sociological research questions. However, in the absence of demographic information, such as gender, many relevant research questions cannot be addressed. To tackle this problem, researchers often rely on automated methods to infer gender from name information provided on the web. However, little is known about the accuracy of existing gender-detection methods and how biased they are against certain sub-populations. In this paper, we address this question by systematically comparing several gender detection methods on a random sample of scientists for whom we know their full name, their gender and the country of their workplace. We further suggest a novel method that employs web-based image retrieval and gender recognition in facial images in order to augment name-based approaches. Our findings show that the performance of name-based gender detection approaches can be biased towards countries of origin and such biases can be reduced by combining name-based an image-based gender detection methods., Comment: 2 pages, WWW conference

In this paper, we study the network of global interconnections between language communities, based on shared co-editing interests of Wikipedia editors, and show that although English is discussed as a potential lingua franca of the digital space, its domination disappears in the network of co-editing similarities, and instead local connections come to the forefront. Out of the hypotheses we explored, bilingualism, linguistic similarity of languages, and shared religion provide the best explanations for the similarity of interests between cultural communities. Population attraction and geographical proximity are also significant, but much weaker factors bringing communities together. In addition, we present an approach that allows for extracting significant cultural borders from editing activity of Wikipedia users, and comparing a set of hypotheses about the social mechanisms generating these borders. Our study sheds light on how culture is reflected in the collective process of archiving knowledge on Wikipedia, and demonstrates that cross-lingual interconnections on Wikipedia are not dominated by one powerful language. Our findings also raise some important policy questions for the Wikimedia Foundation., Comment: 20 pages, 5 figures, 3 tables Best poster award at the NetSciX'16 in Wroclaw, Poland