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Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
ISBN: 978-1-4244-5445-7
pp: 649-652
Zhaonian Zou , Department of Computer Science and Technology, Harbin Institute of Technology, China
Jianzhong Li , Department of Computer Science and Technology, Harbin Institute of Technology, China
Hong Gao , Department of Computer Science and Technology, Harbin Institute of Technology, China
Shuo Zhang , Department of Computer Science and Technology, Harbin Institute of Technology, China
ABSTRACT
Existing studies on graph mining focus on exact graphs that are precise and complete. However, graph data tends to be uncertain in practice due to noise, incompleteness and inaccuracy. This paper investigates the problem of finding top-k maximal cliques in an uncertain graph. A new model of uncertain graphs is presented, and an intuitive measure is introduced to evaluate the significance of vertex sets. An optimized branch-and-bound algorithm is developed to find top-k maximal cliques, which adopts efficient pruning rules, a new searching strategy and effective preprocessing methods. The extensive experimental results show that the proposed algorithm is very efficient on real uncertain graphs, and the top-k maximal cliques are very useful for real applications, e.g. protein complex prediction.
CITATION
Zhaonian Zou, Jianzhong Li, Hong Gao, Shuo Zhang, "Finding top-k maximal cliques in an uncertain graph", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 649-652, doi:10.1109/ICDE.2010.5447891
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