Finding A Summary For All Maximal Cliques
2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2021)(2021)
摘要
The number of maximal cliques could be exponentially large with respect to the number of vertices. A clique summary is a subset of all the maximal cliques and can somehow represent all the maximal cliques. Finding such a summary is deemed important in information distribution, influence estimation, cost-effective marketing, etc. The existing approach that finds a maximal clique summary suffers from long running time due to the excessive number of costly bound calculations that are used to estimate the size of to-be-found cliques during the enumeration process. Furthermore, we found that, sometimes, the bound calculation is not necessary at all. As a result, in order to provide the best study of the problem, we propose four strategies in two directions to speed up the process of finding a maximal clique summary by (1) restricting the bound calculation operation to a particular subset of all search branches and (2) making the best use of the bounds that have been previously calculated. Extensive experiments are conducted on eight real-world datasets to validate our strategies. Results demonstrate that the proposed method can reduce the number of bound calculations by 3 similar to 5 orders of magnitude, and each run of our algorithm can be up to 2.x times faster than the state-of-the-art algorithm while still keeping the summary concise. Our method can potentially benefit other large-output enumeration based problems, such as frequent itemset mining, when a summary of results is needed.
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关键词
maximal clique, clique summary, bound estimation
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