Stochastic Link and Group Detection
by Jeremy Kubica, Andrew Moore, Jeff Schneider, and Yiming Yang
BibTeX:
@inproceedings{kubica2002GDA,
author = "Jeremy Kubica and Andrew Moore and Jeff Schneider and Yiming Yang",
title = "Stochastic Link and Group Detection",
booktitle = "The Eighteenth National Conference on Artificial Intelligence",
month = "Jul",
pages = "798--804",
year = 2002
}
Abstract:
Link detection and analysis has long been important in the social sciences
and in the government intelligence community. A significant effort is
focused on the structural and functional analysis of ``known'' networks.
Similarly, the detection of individual links is important but is usually
done with techniques that result in ``known'' links. More recently the
internet and other sources have led to a flood of circumstantial data that
provide probabilistic evidence of links. Co-occurrence in news articles and
simultaneous travel to the same location are two examples.
We propose a probabilistic model of link generation based on membership in
groups. The model considers both observed link evidence and demographic
information about the entities. The parameters of the model are learned
via a maximum likelihood search. In this paper we describe the model and
then show several heuristics that make the search tractable. We test our
model and optimization methods on synthetic data sets with a known ground
truth and a database of news articles.
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