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Efficient and Progressive Group Steiner Tree Search

Summary: Proposes PrunedDP for GST using optimal-tree decomposition and conditional-tree merging to prune DP search. With progressive A*-search and tight bounds, it yields refinements, huge speedups and lower memory, often locating optimal GST solutions. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5306
Venue
SIGMOD
Year
2016
Pagerank
5.8710857e-05
Overall Rank
6,469 | 55.62%
DOI
10.1145/2882903.2915217

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{li_sigmod16,
        title = {{Efficient and Progressive Group Steiner Tree Search}},
        author = {Li, Rong-Hua and Qin, Lu and Yu, Jeffrey Xu and Mao, Rui},
        series = {{SIGMOD} '16},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2882903.2915217},
        url = {https://dl.acm.org/doi/10.1145/2882903.2915217},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
8,920 Full-Power Graph Querying: State of the Art and Challenges 2023 VLDB 5.3483178e-05
10,370 Fast Optimal Group Steiner Tree Search using GPUs 2026 SIGMOD 5.093636e-05
11,463 Approximating Probabilistic Group Steiner Trees in Graphs 2023 VLDB 5.093636e-05
11,691 Finding Group Steiner Trees in Graphs with both Vertex and Edge Weights 2021 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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