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ABSTRACT
A key challenge for data mining is tackling the problem of mining richly structured datasets, where the objects are linked in some way. Links among the objects may demonstrate certain patterns, which can be helpful for many data mining tasks and are usually hard to capture with traditional statistical models. Recently there has been a surge of interest in this area, fueled largely by interest in web and hypertext mining, but also by interest in mining social networks, security and law enforcement data, bibliographic citations and epidemiological records.
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Irene M. Ong , David Page , Inês Dutra , Vítor Santos Costa, Hyperpaths: extending pathfinding to moded languages, Proceedings of the 4th international workshop on Multi-relational mining, p.57-57, August 21-21, 2005, Chicago, Illinois
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Deept Kumar , Naren Ramakrishnan , Richard F. Helm , Malcolm Potts, Algorithms for storytelling, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, August 20-23, 2006, Philadelphia, PA, USA
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Giles Oatley , Ken Mcgarry , Brian Ewart, Prioritizing of offenders in networks, Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization, p.141-146, September 22-24, 2006, Lisbon, Portugal
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Tim Weninger , William H. Hsu , Jing Xia , Waleed Aljandal, An evolutionary approach to constructive induction for link discovery, Proceedings of the 11th annual conference companion on Genetic and evolutionary computation conference, July 08-12, 2009, Montreal, Québec, Canada
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