Abstract. We describe a methodology for the automatic identification of communities of practice from email logs within an organization. We use a betweenness centrality algorithm that can rapidly find communities within a graph representing information flows. We apply this algorithm to an email corpus of nearly one million messages collected over a two-month span, and show that the method is effective at identifying true communities, both formal and informal, within these scale-free graphs. This approach also enables the identification of leadership roles within the communities. These studies are complemented by a qualitative evaluation of the results in the field.

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Knowledge of how people interact is important in many disciplines, e.g. organizational behavior, social network analysis, information diffusion and knowledge management applications. We are developing methods to automatically and unobtrusively learn the social network structures that arise within human groups based on wearable sensors. At present researchers mainly have to rely on questionnaires, surveys or diaries in order to obtain data on physical interactions between people. In this paper, we show how sensor measurements from the sociometer can be used to build computational models of group interactions. We present results on how we can learn the structure of faceto-face interactions within groups, detect when members are in face-to-face proximity and also when they are having a conversation.

A major impediment to using recommendation systems and collective knowledge for electronic commerce is the reluctance of individuals to reveal preferences in order to find groups of people that share them. An equally important barrier to fluid electronic commerce is the lack of agreed upon trusted third parties. We propose new non-third party mechanisms to overcome these barriers. Our solutions facilitate finding shared preferences, discovering communities with shared values, removing disincentives posed by liabilities, and negotiating on behalf of a group. We adapt known techniques from the cryptographic literature to enable these new capabilities. 1 Introduction With the advent of the World Wide Web and the ease of entry enabled by the Internet, electronic commerce is becoming an increasing reality, with a consequent growth in the number and variety of information providers and e-commerce sites. While this growth generates a diverse set of offerings from which consumers can only be...

Abstract. We review three studies of information flow in social networks that help reveal their underlying social structure, how information spreads among them and why small world experiments work. 1

In this paper, we describe the use of the sociometer, a wearable sensor package, for measuring face-to-face interactions between people. We develop methods for learning the structure and dynamics of human communication networks. Knowledge of how people interact is important in many disciplines, e.g. organizational behavior, social network analysis and knowledge management applications such as expert finding. At present researchers mainly have to rely on questionnaires, surveys or diaries in order to obtain data on physical interactions between people. In this paper, we show how noisy sensor measurements from the sociometer can be used to build computational models of group interactions. Using statistical pattern recognition techniques such as dynamic Bayesian network models we can automatically learn the underlying structure of the network and also analyze the dynamics of individual and group interactions. We present preliminary results on how we can learn the structure of face-to-face interactions within a group, detect when members are in face-to-face proximity and also when they are having a conversation. We also measure the duration and frequency of interactions between people and the participation level of each individual in a conversation.

. In this paper, we present some preliminary results concerning

: A formal model of performance, interaction, and improvisation is described using mutually recursive processes. The recursion captures feedback, mutual recursion captures interaction between players (or larger entities), and the architecture of the recursive network captures hierarchies of interaction. With this model various classes of interaction can be categorized based on the internals of the engaged processes. These can range from simple articulations and embellishments to complex improvisations. The architecture also allows exploration of novel kinds of algorithmic composition. One of particular interest arises via application of game theory: engaged processes can be viewed as players in a game, where currency is manifested as aspects of musical aesthetics, and the rules relate to control of such aesthetics. 1 Introduction More often than not, computer music generation is viewed as a relatively linear process of composition and performance, with performance further broken down...

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of Rochester. He is presently at work on several articles explaining the structure and dynamics of academic systems via mathematical models. Allen’s current research interests include mathematical sociology, the sociology of higher education, academic labor markets, and the academic professions. The ongoing debate over affirmative action reflects intense feelings about the presence and productivity of women and minority faculty in higher education. 1 Supporters of affirmative action, seeing campuses dominated by white males, lobby for diversification. Opponents of “quotas ” may ask if the newcomers can “measure