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.

We present a study of information flow that takes into account the observation that an item relevant to one person is more likely to be of interest to individuals in the same social circle than those outside of it. This is due to the fact that the similarity of node attributes in social networks decreases as a function of the graph distance. An epidemic model on a scale-free network with this property has a finite threshold, implying that the spread of information is limited. We tested our predictions by measuring the spread of messages in an organization and also by numerical experiments that take into consideration the organizational distance among individuals.The problem of information flows in social organizations is relevant to issues of productivity, innovation and the sorting out of useful ideas out of the general chatter of a community. How information spreads determines the speed with which individuals can act and plan their future activities. In particular, email has become the predominant means of communication in the information society. It pervades business, social and scientific exchanges and as such it is a highly relevant area for research on communities and social networks. Not surprisingly, email has been established as an indicator of collaboration and knowledge exchange [1,2,3,4,5]. Email is also a good medium for research because it provides plentiful data on personal communication in an electronic form.Since individuals tend to organize both formally and informally into groups based on their common activities and interests, the way information spreads is affected by the topology of the interaction network, not unlike the spread of a disease among individuals. Thus one would expect that epidemic models on graphs are relevant to the study of information flow in organizations. In particular, recent work on epidemic propagation on scale free networks found that the threshold for an epidemic is zero, implying that a finite fraction of the graph becomes infected for arbitrarily low transmission probabilities [6,7,8]. The presence of additional network structure was found to further influence the spread of disease on scale-free graphs [9, 10, 11].There are, however, differences between information flows and the spread of viruses. While viruses tend to be indiscriminate, infecting any susceptible individual, information is selective and passed by its host only to individuals the host thinks would be interested in it.The information any individual is interested in depends strongly on their characteristics. Furthermore, individuals with similar characteristics tend to associate with one another, a phenomenon known as homophily [12,13,14]. Conversely, individuals many steps removed in a social network on average tend not to have as much in common, as shown in a study [15] of a network of Stanford student homepages and illustrated in Figure 1.We therefore introduce an epidemic model with decay in the transmission probability of a particular piece of in- formation as a function of the distance between t...

Abstract.A study of email responsiveness was conducted to understand how the timing of email responses conveys important information. Interviews and observations explored users' perceptions of how they responded to email and formed expectations of others' responses to them. We identified ways in which users maintain and cultivate a responsiveness image for projecting expectations about their email response. We also discuss other contextual cues people use to discover email responsiveness, which include using other tools such as the calendar and phone, accounting for the amount of work time overlap available, and establishing a pacing between email correspondents. These cues help users develop a sense of when to expect a response and when breakdown has occurred, requiring further action.

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.