Difference between revisions of "2011 AGameTheoreticFrameworkforHeter"

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* 22. Ruggero G. Pensa, Jean-François Boulicaut, Towards Fault-tolerant Formal Concept Analysis, Proceedings of the 9th Conference on Advances in Artificial Intelligence, September 21-23, 2005, Milan, Italy [http://dx.doi.org/10.1007/11558590_22 doi:10.1007/11558590_22]
* 23. Ryan Porter, Eugene Nudelman, Yoav Shoham, Simple Search Methods for Finding a Nash Equilibrium, Proceedings of the 19th National Conference on Artifical Intelligence, p.664-669, July 25-29, 2004, San Jose, California
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* 23. Ryan Porter, Eugene Nudelman, [[Yoav Shoham]], Simple Search Methods for Finding a Nash Equilibrium, Proceedings of the 19th National Conference on Artifical Intelligence, p.664-669, July 25-29, 2004, San Jose, California
 
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* 24. Cecilia M. Procopiuc, Michael Jones, Pankaj K. Agarwal, T. M. Murali, A Monte Carlo Algorithm for Fast Projective Clustering, Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, June 03-06, 2002, Madison, Wisconsin [http://doi.acm.org/10.1145/564691.564739 doi:10.1145/564691.564739]
 
* 25. Kelvin Sim, Jinyan Li, Vivekanand Gopalkrishnan, Guimei Liu, Mining Maximal Quasi-Bicliques to Co-Cluster Stocks and Financial Ratios for Value Investment, Proceedings of the Sixth International Conference on Data Mining, p.1059-1063, December 18-22, 2006 [http://dx.doi.org/10.1109/ICDM.2006.111 doi:10.1109/ICDM.2006.111]
 
* 25. Kelvin Sim, Jinyan Li, Vivekanand Gopalkrishnan, Guimei Liu, Mining Maximal Quasi-Bicliques to Co-Cluster Stocks and Financial Ratios for Value Investment, Proceedings of the Sixth International Conference on Data Mining, p.1059-1063, December 18-22, 2006 [http://dx.doi.org/10.1109/ICDM.2006.111 doi:10.1109/ICDM.2006.111]

Revision as of 14:27, 13 August 2019

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Abstract

Heterogeneous information networks are pervasive in applications ranging from bioinformatics to e-commerce. As a result, unsupervised learning and clustering methods pertaining to such networks have gained significant attention recently. Nodes in a heterogeneous information network are regarded as objects derived from distinct domains such as ' authors' and ' papers'. In many cases, feature sets characterizing the objects are not available, hence, clustering of the objects depends solely on the links and relationships amongst objects. Although several previous studies have addressed information network clustering, shortcomings remain. First, the definition of what constitutes an information network cluster varies drastically from study to study. Second, previous algorithms have generally focused on non-overlapping clusters, while many algorithms are also limited to specific network topologies. In this paper we introduce a game theoretic framework (GHIN) for defining and mining clusters in heterogeneous information networks. The clustering problem is modeled as a game wherein each domain represents a player and clusters are defined as the Nash equilibrium points of the game. Adopting the abstraction of Nash equilibrium points as clusters allows for flexible definition of reward functions that characterize clusters without any modification to the underlying algorithm. We prove that well-established definitions of clusters in 2-domain information networks such as formal concepts, maximal bi-cliques, and noisy binary tiles can always be represented as Nash equilibrium points. Moreover, experimental results employing a variety of reward functions and several real world information networks illustrate that the GHIN framework produces more accurate and informative clusters than the recently proposed NetClus and state of the art MDC algorithms.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2011 AGameTheoreticFrameworkforHeterFaris Alqadah
Raj Bhatnagar
A Game Theoretic Framework for Heterogenous Information Network Clustering10.1145/2020408.20205472011