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Query processing over graph-structured data is enjoying a growing number of applications. A top-k keyword search query on a graph nds the top k answers according to some ranking criteria, where each answer is a substructure of the graph containing all query keywords. Current techniques for supporting such queries on general graphs suffer from several drawbacks, e.g., poor worst-case performance, not taking full advantage of indexes, and high memory requirements. To address these problems, wedoi:10.1145/1247480.1247516 dblp:conf/sigmod/HeWYY07 fatcat:cbrod677w5ex5mhiicfukakrxi