Joshua Rogers Tyler

E-Mail as Spectroscopy: Automated Discovery of Community Structure within Organizations

We describe a method for the automatic identification of communities of practice from e-mail 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 initial e-mail corpus of nearly 1 million messages collected over a 2-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.

Information flow in social groups

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.

Email as spectroscopy: automated discovery of community structure within organizations

We describe a method 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.

SHOCK: communicating with computational messages and automatic private profiles

A computationally enhanced message contains some embedded programmatic components that are interpreted and executed automatically upon receipt. Unlike ordinary text email or instant messages, they make possible a number of useful applications. In this paper, we describe a general and flexible messaging system called SHOCK that extends the functionality of prior computational email systems by allowing XML-encoded SHOCK messages to interact with an automatically created profile of a user. These profiles consist of information about the most common tasks users perform, such as their Web browsing behavior, their conventional email usage, etc. Since users are sensitive about such data, the system is designed with privacy as a central design goal, and employs a distributed peer-to-peer architecture to achieve it. The system is largely implemented with commodity Web technologies and provides both a Web interface as well as one that is tightly integrated with users ordinary email clients. With SHOCK, users can send highly targeted messages without violating others privacy, and engage in structured conversation appropriate to the context without disrupting their existing work practices. We describe our implementation in detail, the most useful novel applications of the system, and our experiences with the system in a pilot field test.