  If I know of a few persons of interest, how can a combination of human
language technology and graph theory help me find other people similarly
interesting? If I know of a few people committing a crime, how can I determine
their co-conspirators? Given a set of actors deemed interesting, we seek other
actors who are similarly interesting. We use a collection of communications
encoded as an attributed graph, where vertices represents actors and edges
connect pairs of actors that communicate. Attached to each edge is the set of
documents wherein that pair of actors communicate, providing content in context
- the communication topic in the context of who communicates with whom. In
these documents, our identified interesting actors communicate amongst each
other and with other actors whose interestingness is unknown. Our objective is
to nominate the most likely interesting vertex from all vertices with unknown
interestingness. As an illustrative example, the Enron email corpus consists of
communications between actors, some of which are allegedly committing fraud.
Some of their fraudulent activity is captured in emails, along with many
innocuous emails (both between the fraudsters and between the other employees
of Enron); we are given the identities of a few fraudster vertices and asked to
nominate other vertices in the graph as likely representing other actors
committing fraud. Foundational theory and initial experimental results indicate
that approaching this task with a joint model of content and context improves
the performance (as measured by standard information retrieval measures) over
either content or context alone.
