In this paper, we present algorithms for ranking nodes in interaction networks. Informally,
they capture the patterns of historical interaction among the nodes and the associated out-
comes. There exists a cardinal ranking over the set of outcomes, characterizing the order of
preference. We argue that ranking of nodes should be influenced by both structural proper-
ties of the networks and the outcome/value created by the interactions. The former aspect is
well studied in social network analysis and is accounted for, in various measures like centrality,
reputation, influence etc. However, the latter aspect is largely unexplored. Our proposed algo-
rithms simultaneously take into account both structural properties as well as the outcomes to
assign ranks for the nodes. We develop a novel eigenvector-like computation that exploits the
structural influences, importance of value creation, and any exogenous information available to
the ranking system. We report experimental results on the IMDB dataset.
By: Kashyap Dixit, S Kameshwaran, Sameep Mehta, Vinayaka Pandit and N Viswanadham
Published in: RI09002 in 2009
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