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Nowadays many data mining/analysis applications use the
graph analysis techniques for decision making. Many of these techniques
are based on the importance of relationships among the interacting units.
A number of models and measures that analyze the relationship importance
(link structure) have been proposed (e.g., centrality, importance
and page rank) and they are generally based on intuition, where the analyst
intuitively decides a reasonable model that fits the underlying data.
In this paper, we address the problem of learning such models directly
from training data. Specifically, we study a way to calibrate a connection
strength measure from training data in the context of reference disambiguation
problem. Experimental evaluation demonstrates that the proposed
model surpasses the best model used for reference disambiguation
in the past, leading to better quality of reference disambiguation.