Help Privacy Policy Disclaimer
  Advanced SearchBrowse





Interactive and Iterative Discovery of Entity Network Subgraphs


Vreeken,  Jilles
Databases and Information Systems, MPI for Informatics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

(Preprint), 4MB

Supplementary Material (public)
There is no public supplementary material available

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.

Cite as: https://hdl.handle.net/11858/00-001M-0000-002B-A939-F
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.