Clustering Large Probabilistic Graphs

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 affiliatio...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 25; no. 2; pp. 325 - 336
Main Authors: Kollios, G., Potamias, M., Terzi, E.
Format: Journal Article
Language:English
Published: IEEE 01.02.2013
Subjects:
Approximation algorithms
Approximation methods
Clustering algorithms
Data mining
Partitioning algorithms
probabilistic databases
probabilistic graphs
Probabilistic logic
Uncertain data
Uncertainty
ISSN:1041-4347
Online Access:Get full text
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Summary: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.
ISSN:1041-4347
DOI:10.1109/TKDE.2011.243