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Behavior Query Discovery in System-Generated Temporal Graphs

Summary: Introduces TGMiner, a discriminative temporal-graph pattern miner that converts system logs into reusable query templates over heterogeneous, time-stamped interactions. Leveraging temporal growth-trend pruning, it prunes redundant patterns, delivering 6–32× speedups with expert-validated patterns at 97% precision and 91% recall. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11505
Venue
VLDB
Year
2016
Pagerank
5.3058708e-05
Overall Rank
9,207 | 36.84%
DOI
10.14778/2850583.2850591

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zong_vldb16,
        title = {{Behavior Query Discovery in System-Generated Temporal Graphs}},
        author = {Zong, Bo and Xiao, Xusheng and Yan, Xifeng and Li, Zhichun and Singh, Ambuj K. and Wu, Zhenyu and Jiang, Guofei and Qian, Zhiyun},
        journal = {PVLDB},
        series = {{VLDB} '16},
        volume = {9},
        number = {4},
        pages = {240--251},
        doi = {10.14778/2850583.2850591},
        url = {https://doi.org/10.14778/2850583.2850591},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,575 AGIS: Fast Approximate Graph Pattern Mining with Structure-Informed Sampling 2026 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 12 of 12 cited papers.

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

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