On effective presentation of graph patterns: a structural representative approach
Proceedings of the 17th ACM conference on Information and knowledge management
A novel approach for efficient supergraph query processing on graph databases
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Frequent subgraph pattern mining on uncertain graph data
Proceedings of the 18th ACM conference on Information and knowledge management
Efficient algorithms for supergraph query processing on graph databases
Journal of Combinatorial Optimization
Efficient discovery of frequent subgraph patterns in uncertain graph databases
Proceedings of the 14th International Conference on Extending Database Technology
Frequent subgraph summarization with error control
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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Several efficient frequent subgraph mining algorithms have been recently proposed. However, the number of frequent graph patterns generated by these graph mining algorithms may be too large to be effectively explored by users, especially when the support threshold is low. In this paper, we propose to summarize frequent graph patterns by a much smaller number of representative graph patterns. Several novel concepts such as delta-cover graph, jump value and delta-jump pattern are proposed for efficiently summarizing frequent graph patterns. Based on the fact that all delta-jump patterns must be representative graph patterns, we propose two efficient algorithms for summarizing frequent graph patterns, RP-FP and RP-GD. The RP-FP algorithm computes representative graph patterns from a set of closed frequent graph patterns, whereas the RP-GD algorithm directly mines representative graph patterns from graph databases. Experimental results show that RP-FP and RP-GD are able to obtain compact summarization in both real and synthetic graph databases. When the number of closed graph patterns is very large, RP-GD is much more efficient than RP-FP, while achieving comparable summarization quality.