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Interest in social network analysis has exploded in the past few years, partly thanks to the advancements in statistical methods and computing for network analysis. A wide range of the methods for network analysis is already covered by existent R packages. However, no comprehensive packages are available to calculate group centrality scores and to identify key players (i.e., those players who constitute the most central group) in a network. These functionalities are important because, for example, many social and health interventions rely on key players to facilitate the intervention. Identifying key players is challenging because players who are individually the most central are not necessarily the most central as a group due to redundancy in their connections. In this paper we develop methods and tools for computing group centrality scores and for identifying key players in social networks. We illustrate the methods using both simulated and empirical examples. The package keyplayer providing the presented methods is available from Comprehensive R Archive Network (CRAN).

This study focuses on mitigating evaluation apprehension, which is usually unavoidable in identifiable social situations, via the constructive use of prominent features of networked technologies. Specifically, this study investigated learners' attitudes towards different user-identity revelation modes, namely, real-identity, anonymity and created-identity, in an online questionconstruction and peer-assessment context. Forty university freshmen, taking a physics laboratory course, participated for one semester in 2007. A learning system called The Question Authoring and Reasoning Knowledge System which allowed students to contribute and benefit from cyclic process of constructing and reviewing questions, was devised. Analysis of the data gathered found that students reacted statistically differently to the modes of real name, anonymity and nickname. Furthermore, participating students adjusted their preferred mode in different roles and circumstances. The data obtained suggest that program developers should embed flexible and versatile capabilities of computer and communication technologies by allowing individuals the opportunity not to be identified or only be identified via a nickname of their choice, so as to help eliminate feelings of embarrassment and uneasiness, which are not psychologically sound and may hinder the learning process.

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