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: 11254
Venue: VLDB
Year: 2016
Pagerank: 4.325244e-05
Overall Rank: 9,575 | 33.39%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,789	Machine Learning Meets Big Spatial Data	2019	VLDB	4.4509194e-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
572	Substructure Similarity Search in Graph Databases	2005	SIGMOD	0.00019887011
1,089	GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph	2014	VLDB	0.00014157922
2,551	NeMa: Fast Graph Search with Label Similarity	2013	VLDB	8.5572574e-05
6,572	Mining Statistically Significant Connected Subgraphs in Vertex Labeled Graphs	2014	SIGMOD	5.005963e-05

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