@article {14476,
	title = {Relationship identification for social network discovery},
	journal = {PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE},
	volume = {22},
	year = {2007},
	month = {2007///},
	pages = {546 - 546},
	abstract = {In recent years, informal, online communication has trans- formed the ways in which we connect and collaborate with friends and colleagues. With millions of individuals commu- nicating online each day, we have a unique opportunity to ob- serve the formation and evolution of roles and relationships in networked groups and organizations. Yet a number of chal- lenges arise when attempting to infer the underlying social network from data that is often ambiguous, incomplete and context-dependent. In this paper, we consider the problem of collaborative network discovery from domains such as intel- ligence analysis and litigation support where the analyst is at- tempting to construct a validated representation of the social network. We specifically address the challenge of relation- ship identification where the objective is to identify relevant communications that substantiate a given social relationship type. We propose a supervised ranking approach to the prob- lem and assess its performance on a manager-subordinate re- lationship identification task using the Enron email corpus. By exploiting message content, the ranker routinely cues the analyst to relevant communications relationships and mes- sage traffic that are indicative of the social relationship.},
	author = {Diehl,C. P and Namata,G. and Getoor, Lise}
}
