AAAI Publications, Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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Identifying Important Nodes in Heterogenous Networks
Oliver Schulte, Fatemeh Riahi, Qing Li

Last modified: 2013-06-29


This is a position paper that presents a new approachto identifying important nodes or entities in a complexheterogeneous network. We provide a novel definitionof an importance score based on a statistical model: Anindividual is important to the extent that including anindividual explicitly in the model improves the data fit of the model more than it increases the model’s com-plexity. We apply techniques from statistical-relationallearning, a recent field that combines AI and machinelearning, to identify statistically important individualsin a scalable manner. We investigate empirically our approach with the OPTA soccer data set for the Englishpremier league.


heterogenous networks, Bayes nets, ranking

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