GARUDA: A System for Large-Scale Mining of Statistically Significant Connected Subgraphs

Summary: GARUDA is a system for scalable mining of statistically significant connected subgraphs in large real-world graphs. It emphasizes a user-friendly GUI, a modular architecture, and a demonstration of real tasks with 8–10× speedups over the MSCS state-of-the-art. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 11442
Venue: VLDB
Year: 2016
Pagerank: 5.2351356e-05
Overall Rank: 9,685 | 33.56%
DOI: 10.14778/3007263.3007281

Incoming Non-self Citations Over Time

Authors

1. Satyajit Bhadange (Indian Institute of Technology Kanpur)
2. Akhil Arora (Xerox Corporation)
3. Arnab Bhattacharya (Indian Institute of Technology Kanpur)

BibTeX Citation

@article{bhadange_vldb16,
        title = {{GARUDA: A System for Large-Scale Mining of Statistically Significant Connected Subgraphs}},
        author = {Bhadange, Satyajit and Arora, Akhil and Bhattacharya, Arnab},
        journal = {PVLDB},
        series = {{VLDB} '16},
        volume = {9},
        number = {13},
        doi = {10.14778/3007263.3007281},
        url = {https://doi.org/10.14778/3007263.3007281},
        year = {2016}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,722	Machine Learning Meets Big Spatial Data	2019	VLDB	5.3769871e-05

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
591	Substructure Similarity Search in Graph Databases	2005	SIGMOD	0.0001603683
1,085	GraMI: Frequent Subgraph and Pattern Mining in a Single Large Graph	2014	VLDB	0.0001225302
2,285	NeMa: Fast Graph Search with Label Similarity	2013	VLDB	8.8052998e-05
7,231	Mining Statistically Significant Connected Subgraphs in Vertex Labeled Graphs	2014	SIGMOD	5.6664362e-05

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