56.An Optimized Query Processing Method for Graph Similarity

Query processing for graph similarity is one of the hot issues in the field of graph data management. The current studies measure the similarity between graphs based on the edit distance, and adopt a filter-and-verify strategy to determine the edit distance between graphs. Considering that existing query methods of graph similarity will return a large number of candidate graphs in the filtering stage, and work inefficiently on computing edit distance between graphs in the verifying stage, we propose an effective satisfiability match filtering strategy based on the filtering processing of the existing methods to reduce the number of candidates and enhance the filtering effect, which takes into account the features of the partition and its match in query graph. Moreover, we propose an algorithm to compute the actual value of edit distance between data graph and query graph, which selects the matched partition as the start and extends the data graph in an appropriate order to improve the efficiency in the verifying stage. The experimental results on real datasets show that our method works better than the state-of-the-art algorithms in both filtering and verifying stages, and is highly efficient in the overall performance.

[1]  Min Wang,et al.  Efficient Parallel Processing of Distance Join Queries Over Distributed Graphs , 2015, IEEE Transactions on Knowledge and Data Engineering.

[2]  Liu Yanbing,et al.  A survey on Succinct Representation of Graph Data , 2014 .

[3]  Wang Yong.  Research and advances on graph data mining , 2012 .

[4]  Randy Goebel,et al.  A Visual Data Mining Approach to Find Overlapping Communities in Networks , 2009, 2009 International Conference on Advances in Social Network Analysis and Mining.

[5]  David Dagan Feng,et al.  Graph-based retrieval of PET-CT images using vector space embedding , 2013, Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems.

[6]  Lei Zou,et al.  Efficient Graph Similarity Search Over Large Graph Databases , 2015, IEEE Transactions on Knowledge and Data Engineering.

[7]  Anthony K. H. Tung,et al.  Comparing Stars: On Approximating Graph Edit Distance , 2009, Proc. VLDB Endow..

[8]  Lubomir Stanchev,et al.  Semantic search using a similarity graph , 2015, Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015).

[9]  Yanghua Xiao,et al.  Structure-based graph distance measures of high degree of precision , 2008, Pattern Recognit..

[10]  Horst Bunke,et al.  On a relation between graph edit distance and maximum common subgraph , 1997, Pattern Recognit. Lett..

[11]  Yifan Chen,et al.  Practising Scalable Graph Similarity Joins in MapReduce , 2014, 2014 IEEE International Congress on Big Data.

[12]  Xuelong Li,et al.  Image categorization: Graph edit distance+edge direction histogram , 2008, Pattern Recognit..

[13]  Xuemin Lin,et al.  Efficient processing of graph similarity queries with edit distance constraints , 2013, The VLDB Journal.

[14]  Jianzhuang Liu,et al.  A Graph-Based Method for Face Identification from a Single 2D Line Drawing , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Ge Yu,et al.  Efficiently Indexing Large Sparse Graphs for Similarity Search , 2012, IEEE Transactions on Knowledge and Data Engineering.

[16]  Qing Liu,et al.  A Partition-Based Approach to Structure Similarity Search , 2013, Proc. VLDB Endow..

[17]  Chen Zhang,et al.  An Improved Invariant for Matching Molecular Graphs Based on VF2 Algorithm , 2015, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[18]  Kaspar Riesen,et al.  Speeding Up Graph Edit Distance Computation through Fast Bipartite Matching , 2011, GbRPR.

[19]  Shuai Li,et al.  An Efficient Approach to Generating Location-Sensitive Recommendations in Ad-hoc Social Network Environments , 2015, IEEE Transactions on Services Computing.