Efficient algorithms for generalized subgraph query processing
Proceedings of the 21st ACM international conference on Information and knowledge management
CTrace: semantic comparison of multi-granularity process traces
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Graph similarity search with edit distance constraint in large graph databases
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Efficient processing of graph similarity queries with edit distance constraints
The VLDB Journal — The International Journal on Very Large Data Bases
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Graphs are widely used to model complicated data semantics in many applications in bioinformatics, chemistry, social networks, pattern recognition, etc. A recent trend is to tolerate noise arising from various sources, such as erroneous data entry, and find similarity matches. In this paper, we study the graph similarity join problem that returns pairs of graphs such that their edit distances are no larger than a threshold. Inspired by the q-gram idea for string similarity problem, our solution extracts paths from graphs as features for indexing. We establish a lower bound of common features to generate candidates. An efficient algorithm is proposed to exploit both matching and mismatching features to improve the filtering and verification on candidates. We demonstrate the proposed algorithm significantly outperforms existing approaches with extensive experiments on publicly available datasets.