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Summary: Ranking on Graph Data
Shivani Agarwal SHIVANI@CSAIL.MIT.EDU
Computer Science and AI Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Abstract
In ranking, one is given examples of order rela-
tionships among objects, and the goal is to learn
from these examples a real-valued ranking func-
tion that induces a ranking or ordering over the
object space. We consider the problem of learn-
ing such a ranking function when the data is rep-
resented as a graph, in which vertices correspond
to objects and edges encode similarities between
objects. Building on recent developments in reg-
ularization theory for graphs and corresponding
Laplacian-based methods for classification, we
develop an algorithmic framework for learning
ranking functions on graph data. We provide
generalization guarantees for our algorithms via
recent results based on the notion of algorithmic
stability, and give experimental evidence of the
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