547 research outputs found
Assessing Centrality Without Knowing Connections
We consider the privacy-preserving computation of node influence in
distributed social networks, as measured by egocentric betweenness centrality
(EBC). Motivated by modern communication networks spanning multiple providers,
we show for the first time how multiple mutually-distrusting parties can
successfully compute node EBC while revealing only differentially-private
information about their internal network connections. A theoretical utility
analysis upper bounds a primary source of private EBC error---private release
of ego networks---with high probability. Empirical results demonstrate
practical applicability with a low 1.07 relative error achievable at strong
privacy budget on a Facebook graph, and insignificant
performance degradation as the number of network provider parties grows.Comment: Full report of paper appearing in PAKDD202
Egocentric Social Network Structure, Health, and Pro-Social Behaviors in a National Panel Study of Americans
Using a population-based, panel survey, we study how egocentric social networks change over time, and the relationship between egocentric network properties and health and pro-social behaviors. We find that the number of prosocial activities is strongly positively associated with having more friends, or an increase in degree, with approximately 0.04 more prosocial behaviors expected for every friend added. Moreover, having more friends is associated with an improvement in health, while being healthy and prosocial is associated with closer relationships. Specifically, a unit increase in health is associated with an expected 0.45 percentage-point increase in average closeness, while adding a prosocial activity is associated with a 0.46 percentage-point increase in the closeness of oneâs relationships. Furthermore, a tradeoff between degree and closeness of social contacts was observed. As the number of close social contacts increases by one, the estimated average closeness of each individual contact decreases by approximately three percentage-points. The increased awareness of the importance of spillover effects in health and health care makes the ascertainment of egocentric social networks a valuable complement to investigations of the relationship between socioeconomic factors and health
Studying Fake News via Network Analysis: Detection and Mitigation
Social media for news consumption is becoming increasingly popular due to its
easy access, fast dissemination, and low cost. However, social media also
enable the wide propagation of "fake news", i.e., news with intentionally false
information. Fake news on social media poses significant negative societal
effects, and also presents unique challenges. To tackle the challenges, many
existing works exploit various features, from a network perspective, to detect
and mitigate fake news. In essence, news dissemination ecosystem involves three
dimensions on social media, i.e., a content dimension, a social dimension, and
a temporal dimension. In this chapter, we will review network properties for
studying fake news, introduce popular network types and how these networks can
be used to detect and mitigation fake news on social media.Comment: Submitted as a invited book chapter in Lecture Notes in Social
Networks, Springer Pres
Organisational participation and women - an attitude problem?
Employee participation is a dynamic and contested area of organisational behaviour, attracting continuing academic, practitioner and policy interest and debate. This chapter focuses on organisational participation and women
Organizational factors and depression management in community-based primary care settings
Abstract Background Evidence-based quality improvement models for depression have not been fully implemented in routine primary care settings. To date, few studies have examined the organizational factors associated with depression management in real-world primary care practice. To successfully implement quality improvement models for depression, there must be a better understanding of the relevant organizational structure and processes of the primary care setting. The objective of this study is to describe these organizational features of routine primary care practice, and the organization of depression care, using survey questions derived from an evidence-based framework. Methods We used this framework to implement a survey of 27 practices comprised of 49 unique offices within a large primary care practice network in western Pennsylvania. Survey questions addressed practice structure (e.g., human resources, leadership, information technology (IT) infrastructure, and external incentives) and process features (e.g., staff performance, degree of integrated depression care, and IT performance). Results The results of our survey demonstrated substantial variation across the practice network of organizational factors pertinent to implementation of evidence-based depression management. Notably, quality improvement capability and IT infrastructure were widespread, but specific application to depression care differed between practices, as did coordination and communication tasks surrounding depression treatment. Conclusions The primary care practices in the network that we surveyed are at differing stages in their organization and implementation of evidence-based depression management. Practical surveys such as this may serve to better direct implementation of these quality improvement strategies for depression by improving understanding of the organizational barriers and facilitators that exist within both practices and practice networks. In addition, survey information can inform efforts of individual primary care practices in customizing intervention strategies to improve depression management.http://deepblue.lib.umich.edu/bitstream/2027.42/78269/1/1748-5908-4-84.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78269/2/1748-5908-4-84-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78269/3/1748-5908-4-84.pdfPeer Reviewe
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Peer norm guesses and self-reported attitudes towards performance-related pay
Due to a variety of reasons, people see themselves differently from how they see others. This basic asymmetry has broad consequences. It leads people to judge themselves and their own behavior differently from how they judge others and othersâ behavior. This research, first, studies the perceptions and attitudes of Greek Public Sector employees towards the introduction of Performance-Related Pay (PRP) systems trying to reveal whether there is a divergence between individual attitudes and guesses on peersâ attitudes. Secondly, it is investigated whether divergence between own self-reported and peer norm guesses could mediate the acceptance of the aforementioned implementation once job status has been controlled for. This study uses a unique questionnaire of 520 observations which was designed to address the questions outlined in the preceding lines. Our econometric results indicate that workers have heterogeneous attitudes and hold heterogeneous beliefs on othersâ expectations regarding a successful implementation of PRP. Specifically, individual perceptions are less skeptical towards PRP than are beliefs on othersâ attitudes. Additionally, we found that managers are significantly more optimistic than lower rank employees regarding the expected success of PRP systems in their jobs. However, they both expect their peers to be more negative than they themselves are
Academic Performance and Behavioral Patterns
Identifying the factors that influence academic performance is an essential
part of educational research. Previous studies have documented the importance
of personality traits, class attendance, and social network structure. Because
most of these analyses were based on a single behavioral aspect and/or small
sample sizes, there is currently no quantification of the interplay of these
factors. Here, we study the academic performance among a cohort of 538
undergraduate students forming a single, densely connected social network. Our
work is based on data collected using smartphones, which the students used as
their primary phones for two years. The availability of multi-channel data from
a single population allows us to directly compare the explanatory power of
individual and social characteristics. We find that the most informative
indicators of performance are based on social ties and that network indicators
result in better model performance than individual characteristics (including
both personality and class attendance). We confirm earlier findings that class
attendance is the most important predictor among individual characteristics.
Finally, our results suggest the presence of strong homophily and/or peer
effects among university students
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