Publication Details

Recovering Social Networks From Massive Track Datasets

by Connolly, C. I. and Burns, J. B. and Bui, H. H.

Technical Note 564
Institution: AI Center, SRI International
Address: 333 Ravenswood Ave., Menlo Park, CA 94025
October 2007.

Order an AIC Technical Note

Abstract

Analysis of massive track datasets is a challenging problem, especially when examining n-way relations inherent in social networks. In this paper, we use the Mitsubishi track database to examine the usefulness of three types of interaction features observable in tracklet networks. We explore ways in which social network information can be extracted and visualized using a statistical sampling of these features from a very large track dataset, with very little ground truth or outside knowledge. Special attention is given to methods that are likely to scale well beyond the size of the Mitsubishi dataset.

Electronic Copies

Adobe PDF

BibTeX

EndNote

Associated Projects

	Cognitive Assistant that Learns and Organizes As part of DARPA’s Personalized Assistant that Learns (PAL) program, SRI and team members are working on developing a next-generation "Cognitive Agent that Learns and Organizes" (CALO).
	Open Source Three-Dimensional Image Understanding System FREEDIUS is a portable, open-source image understanding workbench. It is the successor to the 3DIUS/CME system.

AIC Personnel

	Name	Title	E-mail
	Bui, Hung H	Senior Computer Scientist
	Burns, Brian J	Computer Scientist
	Connolly, Christopher I.	Senior Computer Scientist