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Substructure Similarity Search in Graph Databases

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The document proposes Grafil, a graph similarity search technique that models query graphs as feature sets to filter candidate matches. Grafil indexes feature occurrences and estimates maximum possible feature misses to filter dissimilar graphs. It selects discriminative features and groups them into filters to balance coverage and selectivity. An evaluation on chemical and synthetic graphs shows Grafil outperforms techniques using individual edges or full query graphs as features.

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Substructure Similarity Search in Graph Databases SIGMOD ‘05 Xifeng Yan, Philip S. Yu, Jiawei Han Reporter ： Chang-Yang Chen 2005/10/18
Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example
Contribution ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Related Work ,[object Object],[object Object],[object Object]
Preliminary Concepts ,[object Object],[object Object],[object Object],[object Object],[object Object]
Preliminary Concepts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Structure Filtering ,[object Object]
Feature-Graph Matrix Index ,[object Object],[object Object]
Framework ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Feature Miss Estimation ,[object Object]
Feature Miss Estimation ,[object Object],[object Object],[object Object]
Feature Miss Estimation
Estimation Refinement ,[object Object],[object Object],[object Object]
Estimation Refinement
Frequency Difference
Feature Set Selection ,[object Object],[object Object],[object Object]
Feature Set Selection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Multi-filter composition strategy ,[object Object],[object Object]
Algorithm Implementation ,[object Object],[object Object],base clustering
Empirical Study ,[object Object],[object Object],[object Object],[object Object],[object Object]
Experiments on the Chemical Compound Dataset
Experiments on the Chemical Compound Dataset ,[object Object]
Experiments on the Synthetic Datasets ,[object Object],[object Object],[object Object],[object Object],[object Object]
Experiments on the Synthetic Datasets
Conclusion ,[object Object],[object Object],[object Object]

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