Social Network

Previous qualitative studies show that when the formal organization of a school and patterns of informal interaction are aligned, faculty and leaders in a school are better able to coordinate instructional change. This article combines social network analysis with interview data to analyze how well the formal and informal aspects of a school’s social context are aligned. The focus is on two elementary schools engaged in initiatives aimed to use data to inform instructional decision making. The multimethod case study integrated findings from questionnaire and interview data. Data were collected over two years from case study schools. By fitting multilevel social selection models to longitudinal social network data collected from surveys, the authors estimated the relative influence of formal and informal processes on patterns of advice giving in each school. They used interview data to contextualize and corroborate findings. The social selection models they fit revealed distinct patt...

Scholars have long recognized the potential of Internet-based communication technologies for improving network research—potential that, to date, remains largely underexploited. In the first half of this paper, we introduce a new public dataset based on manipulations and ...

Topic detection with large and noisy data collections such as social media must address both scalability and accuracy challenges. KeyGraph is an efficient method that improves on current solutions by considering keyword cooccurrence. We show that KeyGraph has similar accuracy when compared to state-of-the-art approaches on small, well-annotated collections, and it can successfully filter irrelevant documents and identify events in large and noisy social media collections. An extensive evaluation using Amazon’s Mechanical Turk demonstrated the increased accuracy and high precision of KeyGraph, as well as superior runtime performance compared to other solutions.

Recent years have witnessed a surge in self-organizing voluntary teams collaborating online to produce goods and services. Motivated by this phenomenon, this research investigates how these teams are formed and how individuals make decisions about which teams to join in the context of open source software development (OSSD). The focus of this paper is to explore how the collaborative network affects developers' choice of newly initiated OSS projects to participate in. More specifically, by analyzing software project data from real-world OSSD projects, we empirically test the impact of past collaborative ties with and perceived status of project members in the network on the self-assembly of OSSD teams. Overall, we find that a developer is more likely to join a project when he has strong collaborative ties with its initiator. We also find that perceived status of the noninitiator members of a project influences its probability of attracting developers. We discuss the implications of our results with respect to self-organizing teams and OSSD.

To increase communication and collaboration opportunities, members of a community must be aware of the social networks that exist within that community. This paper describes a social network monitoring system – the KIWI system – that enables users to register their interactions and visualize their social networks. The system was implemented in a distributed research community and the results have shown that KIWI facilitates collecting information about social interactions. Furthermore, the visualization of the social networks, given as feedback, appeared to have a positive impact on the group, augmenting their social network awareness.

... 1999; Roberts 1998). Although the Dutch in New Zealand are, after the Samoan and Chinese communities, the third largest nonindigenous ethnic group among non-English speaking ethnicities, opportunities for language use and language contact are limited. ...

This article explores the determinants of corruption in transition economies of the post-Soviet Union, Central-Eastern Europe, and Western industrialized states. We look in-depth at the East–West gap in corruption, and why entrepreneurs and small business owners become engaged in corrupt deals. Part of the answers lie in the country-specific formal and informal institutional make-up. The likelihood of engaging in corruption is influenced by the lower efficiency of financial and legal institutions and the lack of their enforcements. Also, viewing illegal business activities as a widespread business practice provides the rationale for entrepreneurs to justify their own corrupt activities. Moreover, closed social networks with family, friends, and national bureaucrats reduce the opportunism of the contracting party of the corrupt deal, thus providing breeding grounds for corruption.

Empirical studies of citizen communication networks and participation go as far back as the 1940s, with a bolder focus in political—not civic—activities. A consistent finding reveals that individuals with larger networks are more engaged than those with smaller networks. This article expands this line of work with a number of novel tests. First, it compares the predictive power of online

Local assortativity has been recently proposed as a measure to analyse complex networks. It has been noted that the Internet Autonomous System level networks show a markedly different local assortativity profile to most biological and social networks. In this paper we show that, even though several Internet growth models exist, none of them produce the local assortativity profile that can be observed in the real AS networks. We introduce a new generic growth model which can produce a linear local assortativity profile similar to that of the Internet. We verify that this model accurately depicts the local assortativity profile criteria of Internet, while also satisfactorily modelling other attributes of AS networks already explained by existing models.

In this paper we develop a model of growth and social capital, and test it using data from the European Value Survey (EVS). Following Putnam’s distinction between bonding and bridging social capital, we model social capital as participation in two types of social networks: first, closed networks of family and friends, and, second, open networks that bridge different communities. Agents have a preference for social interaction, which they trade off against material well-being. Participation in both social networks is time-consuming and comes at the cost of participation in the formal economic sphere and working time. Through this channel, higher levels of social capital may crowd out economic growth. In addition, participation in intercommunity networks reduces incentives for rent seeking and cheating. Through this channel, higher level of bridging social capital may enhance economic growth. Testing the model, we find that regional differences in materialistic attitudes and the value...

The use and abuse of licit and illicit substances in adolescence is a national public health concern. This behavior impairs healthy development for many adolescents in the United States. Although not every adolescent who becomes a regular user of licit and illicit substances will develop a substance abuse disorder, all adolescents using these substances can experience a life-threatening outcome. Understanding the epidemiology and social profile of adolescent substance use, namely the risk and protective factors and the environmental and genetic factors, is essential to the development of strategies for prevention. There are many methods that can be employed to better assess environments in which adolescents live. The method discussed in this paper is descriptive and utilizes Geographic Information Systems (GIS) technology. The primary goal of this paper is to illustrate and describe an analysis of substance using and non-substance using adolescents, their social networks, the risky and protective settings where they socialize, and the relationship of these variables to health outcomes such as substance use, depression, and stress. Published data from the researchers’ recent investigation examine the effect of social network affiliations and geographical risk factors on drug involvement and illustrate how these factors may then be incorporated into prevention and intervention planning, especially in medical settings.