Abstract:
Subgraph matching finds all data graph embeddings that match a query graph. Recent algorithms create a tree-structured index on the data graph based on the query graph, order the vertices path-by-path, and enumerate the embeddings in matching order.
Due to the lack of edge consideration across tree paths, path-based ordering and tree-structured index-based enumeration limit performance. We propose a cost model-based matching order that considers query vertex edges and candidate numbers to solve this problem.
We also use a bigraph index to enumerate candidate vertices and their selected neighbors in the data graph in matching order. Our method outperforms the state-of-the-art by orders of magnitude in real-world and synthetic datasets.
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