Measuring the Generalized Friendship Paradox in Networks with Quality-dependent Connectivity

The friendship paradox is a sociological phenomenon stating that most people have fewer friends than their friends do. The generalized friendship paradox refers to the same observation for attributes other than degree, and it has been observed in Twitter and scientific collaboration networks. This paper takes an analytical approach to model this phenomenon. We consider a preferential attachment-like network growth mechanism governed by both node degrees and `qualities'. We introduce measures to quantify paradoxes, and contrast the results obtained in our model to those obtained for an uncorrelated network, where the degrees and qualities of adjacent nodes are uncorrelated. We shed light on the effect of the distribution of node qualities on the friendship paradox. We consider both the mean and the median to measure paradoxes, and compare the results obtained by using these two statistics

Online Privacy as a Collective Phenomenon

The problem of online privacy is often reduced to individual decisions to hide or reveal personal information in online social networks (OSNs). However, with the increasing use of OSNs, it becomes more important to understand the role of the social network in disclosing personal information that a user has not revealed voluntarily: How much of our private information do our friends disclose about us, and how much of our privacy is lost simply because of online social interaction? Without strong technical effort, an OSN may be able to exploit the assortativity of human private features, this way constructing shadow profiles with information that users chose not to share. Furthermore, because many users share their phone and email contact lists, this allows an OSN to create full shadow profiles for people who do not even have an account for this OSN. We empirically test the feasibility of constructing shadow profiles of sexual orientation for users and non-users, using data from more than 3 Million accounts of a single OSN. We quantify a lower bound for the predictive power derived from the social network of a user, to demonstrate how the predictability of sexual orientation increases with the size of this network and the tendency to share personal information. This allows us to define a privacy leak factor that links individual privacy loss with the decision of other individuals to disclose information. Our statistical analysis reveals that some individuals are at a higher risk of privacy loss, as prediction accuracy increases for users with a larger and more homogeneous first- and second-order neighborhood of their social network. While we do not provide evidence that shadow profiles exist at all, our results show that disclosing of private information is not restricted to an individual choice, but becomes a collective decision that has implications for policy and privacy regulation

Study of Network Paradoxes

Social networks are characterized by their user-oriented nature, and interactive, community-driven and emotion based content. There are some known paradoxes about social network of which `friendship paradox' is the most famous one. According to this paradox, on an average your friends have more friends than you have. This paradox can have either of the two types of origin: statistical or behavioral. A statistical origin can be determined based on the mathematical properties of social connectivity such as mean and median. The research analyzes this problem by using data from one of the online social networks We first show that the social connectivity data does not satisfy Benford's law. This fact and other statistical analysis performed by us establish that the friendship paradox data from social networks has a large behavioral component.Computer Scienc

The Effects of Online Social Networking on Social Connectedness and Friendship Quality Among Adolescents

The emergence of social networking sites (SNSs) has led to marked shifts in the ways that individuals communicate, share, and acquire information. Present-day adolescents are the first generation to grow up with these technologies and are among the most frequent users (Shapiro & Margolin, 2014). Although the technological landscape continues to evolve, the impact it has on aspects of adolescent development remains poorly understood. This study examined the possible relationship between SNS use and perceptions of social connectedness and friendship quality in a sample of Canadian and American adolescents. A self-report questionnaire developed by the author was utilized to examine the ways participants use SNSs. The Social Connectedness Scale (Lee, Draper, & Lee, 2001) and a modified version of this scale were used to measure offline and online social connectedness. The Friendship Quality Scale (FQS; Bukowski, Hoza, & Boivin, 1994) and a modified version of this scale were used to measure aspects of offline and online friendship quality. The results showed a nonsignificant relationship between the amount of time adolescents spent on SNSs for both friendship quality and social connectedness. The ways that adolescents used SNSs (e.g., for communication or non-communication purposes) were also found to be nonsignificant in their relation to friendship quality and social connectedness. These results are likely due to the variability in the ways that participants spent their time online as well as the overlap between offline and online domains. The finding that using SNSs for communication purposes did not impact friendship quality or social connectedness is likely due to the changing nature of SNSs, which facilitates visually-based information sharing and can result in superficial communication. Limitations of the study and future directions are discussed

Performance gains and losses from network centrality in cluster located firms: a longitudinal study

This paper develops and tests theoretically derived arguments on the performance trade-offs that arise when firms located inside geographical clusters broaden their cluster networks and increase their centrality. Using three-year longitudinal data gathered on a sample of 89 small media firms located in a geographical cluster of Northern Italy, we model growth in revenues and in employees as a function of their centrality in different types of networks. We find an inverted U-shaped effect of centrality across all types of networks. We also find strong evidence of negative interactivity between network types in predicting sales and employee growth. This result not only concurs with the view that centrality brings tangible and intangible benefits, but also provides empirical support for the contention that centrality fosters dispositions and disturbances that undermine performance

Some novel approaches to economic problems.

In chapter 1, I investigate the role of social connections in the formation of political coalitions utilizing the county-to-county Social Connectedness Index (SCI) from Facebook. Using a generalized fixed effects model I estimate the impact of the SCI-weighted network vote margin on focal county vote margins. Democrat-leaning counties (above-median Democrat-net-Republican percentage) respond robustly to the influence of their social network in all years examined. Republican-leaning counties accelerated coalition formation in years after the 2012 election. Since voting for the US president happens at the same time in all counties, I also use a 2SLS analysis to deal with the endogeneity that stems from this simultaneous action. I construct the instrument for the endogenous network-wide vote margin variable by utilizing the network structure of the dataset. The empirical analysis in this paper confirms a high and growing level of political polarization in the US. Chapter 2 creates a new measure of social capital that combines bridging and bonding social capital. New data from the Facebook user network makes this possible. I use the new measure to reassess the role of social capital on a variety of outcomes previously studied in the literature. I use the network topology of social connections and within-county associational membership levels to measure county-level social capital. This measure allows one to combine two different types of measures into a comprehensive one. Since bonding and bridging social capital are by nature different, the ability to measure these two values from different sources and then combine them improves my measure of social capital over previous ones. Results show that my measure is a superior predictor of a variety of social and economic outcomes. Chapter 3 is coauthored with Cheng Ma. In chapter 3, we test the effect of additional children in a family on health and educational outcomes. Estimating this effect is complicated by the endogeneity of family size. We use the variation in the severity of the effect of the one-child-policy in China to extract exogenous variation from the China Health and Nutrition Survey in the same country. After finding a negative effect of family size on health and educational outcomes of children we use a newly developed machine learning approach. Generalized random forests allows us to look at the heterogeneity in treatment effects in the quantity-quality trade-off. We find robust negative treatment effects of additional children on health outcomes but only mild effects on educational attainment. The machine learning algorithm finds mother's age and parent's education level play a large role in the negative quantity-quality trade-off. Pinpointing the factors that exacerbate the negative effect of additional children on child quality can aid future policy decisions

INVESTIGATING THE CO-EVOLUTION OF INDIVIDUAL AND NETWORK-LEVEL RECOVERY CAPITAL: A DYNAMIC SOCIAL NETWORK ANALYSIS TESTING NETWORK COHESION AND EXCHANGE THEORIES

Historically, treatment professionals, researchers, and policymakers widely regarded substance use disorders (SUDs) as acute conditions that patients could “recover” from after a single treatment. Recent efforts have redefined recovery as a lifelong, dynamic process that involves improvements in multiple domains over time. Thus, recovery capital frameworks and theory have gained momentum as a way to operationalize recovery from SUDs. Recovery capital is a multifaceted framework with theoretical underpinnings in the social capital literature that provides a way of conceptualizing and measuring the complexities of the recovery process. While the literature on recovery capital has grown significantly since its conception, the extant research has focused on investigating recovery capital at the individual-level and not on how it is developed contextually. The current longitudinal study sought to advance understanding of how recovery capital is developed using social network analysis while testing network cohesion, social exchange, and generalized exchange theories. Stochastic Actor Oriented Modeling was conducted on individuals recovering from SUDs (N = 627) residing in 42 recovery homes. Findings indicated that while cohesion, social exchanges, and generalized exchanges were prevalent across various types of networks, these network-level effects had no influences on changes in the individual-level of recovery capital. However, a dyadic-level effect was found, indicating that residents’ individual-level recovery capital increased when they were directly connected to those rich in recovery capital. Additionally, compared to men, women had slower increases in their recovery capital over time. The theoretical and practical implications and recommendations for future research are discussed