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  Interactive and Iterative Discovery of Entity Network Subgraphs

Wu, H., Sun, M., Vreeken, J., Tatti, N., North, C., & Ramakrishnan, N. (2016). Interactive and Iterative Discovery of Entity Network Subgraphs. Retrieved from http://arxiv.org/abs/1608.03889.

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arXiv:1608.03889.pdf (Preprint), 4MB
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 Urheber:
Wu, Hao1, Autor
Sun, Maoyuan1, Autor
Vreeken, Jilles2, Autor           
Tatti, Nikolaj1, Autor
North, Chris1, Autor
Ramakrishnan, Naren1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

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Schlagwörter: cs.SI,Computer Science, Databases, cs.DB
 Zusammenfassung: Graph mining to extract interesting components has been studied in various guises, e.g., communities, dense subgraphs, cliques. However, most existing works are based on notions of frequency and connectivity and do not capture subjective interestingness from a user's viewpoint. Furthermore, existing approaches to mine graphs are not interactive and cannot incorporate user feedbacks in any natural manner. In this paper, we address these gaps by proposing a graph maximum entropy model to discover surprising connected subgraph patterns from entity graphs. This model is embedded in an interactive visualization framework to enable human-in-the-loop, model-guided data exploration. Using case studies on real datasets, we demonstrate how interactions between users and the maximum entropy model lead to faster and explainable conclusions.

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Sprache(n): eng - English
 Datum: 2016-08-122016
 Publikationsstatus: Online veröffentlicht
 Seiten: 10 p.
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 Identifikatoren: arXiv: 1608.03889
URI: http://arxiv.org/abs/1608.03889
BibTex Citekey: Wu1608.03889
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