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Clustering Large Probabilistic Graphs

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3 Author(s)
Kollios, G. ; Comput. Sci. Dept., Boston Univ., Boston, MA, USA ; Potamias, M. ; Terzi, E.

We study the problem of clustering probabilistic graphs. Similar to the problem of clustering standard graphs, probabilistic graph clustering has numerous applications, such as finding complexes in probabilistic protein-protein interaction (PPI) networks and discovering groups of users in affiliation networks. We extend the edit-distance-based definition of graph clustering to probabilistic graphs. We establish a connection between our objective function and correlation clustering to propose practical approximation algorithms for our problem. A benefit of our approach is that our objective function is parameter-free. Therefore, the number of clusters is part of the output. We also develop methods for testing the statistical significance of the output clustering and study the case of noisy clusterings. Using a real protein-protein interaction network and ground-truth data, we show that our methods discover the correct number of clusters and identify established protein relationships. Finally, we show the practicality of our techniques using a large social network of Yahoo! users consisting of one billion edges.

Published in:

Knowledge and Data Engineering, IEEE Transactions on  (Volume:25 ,  Issue: 2 )

Date of Publication:

Feb. 2013

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