STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Bidirectional expansion for keyword search on graph databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
BLINKS: ranked keyword searches on graphs
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
STAR: Steiner-Tree Approximation in Relationship Graphs
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
A sketch-based distance oracle for web-scale graphs
Proceedings of the third ACM international conference on Web search and data mining
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
Fast and accurate estimation of shortest paths in large graphs
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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Finding the minimum connected subtree of a graph that contains a given set of nodes (i.e., the Steiner tree problem) is a fundamental operation in keyword search in graphs, yet it is known to be NP-hard. Existing approximation techniques either make use of the heavy indexing of the graph, or entirely rely on online heuristics. In this paper we bridge the gap between these two extremes and present a scalable landmark-based index structure that, combined with a few lightweight online heuristics, yields a fast and accurate approximation of the Steiner tree. Our solution handles real-world graphs with millions of nodes and provides an approximation error of less than 5% on average.