NScale: Neighborhood-centric Analytics on Large Graphs

Summary: NScale introduces a neighborhood-centric, end-to-end framework for distributed multi-hop graph analytics, letting users program over subgraphs rather than vertices. Its GEL extraction/loading and overlap-aware execution reduce communication, memory, and cloud cost by orders of magnitude. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 11036
Venue: VLDB
Year: 2014
Pagerank: 5.3949716e-05
Overall Rank: 8,639 | 40.73%
DOI: 10.14778/2733004.2733019

Incoming Non-self Citations Over Time

Authors

1. Abdul Quamar (University of Maryland)
2. Amol Deshpande (University of Maryland)
3. Jimmy Lin (University of Maryland)

BibTeX Citation

@article{quamar_vldb14,
        title = {{NScale: Neighborhood-centric Analytics on Large Graphs}},
        author = {Quamar, Abdul and Deshpande, Amol and Lin, Jimmy},
        journal = {PVLDB},
        series = {{VLDB} '14},
        volume = {7},
        number = {13},
        pages = {1673--1684},
        doi = {10.14778/2733004.2733019},
        url = {https://doi.org/10.14778/2733004.2733019},
        year = {2014}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,323	TurboGraph++: A Scalable and Fast Graph Analytics System	2018	SIGMOD	6.7608401e-05
5,567	iTurboGraph: Scaling and Automating Incremental Graph Analytics	2021	SIGMOD	6.1709411e-05
6,450	Big Graph Analytics Systems	2016	SIGMOD	5.8753826e-05

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
20	Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud	2012	VLDB	0.00056944564
487	From "Think Like a Vertex" to "Think Like a Graph"	2014	VLDB	0.00017645653
500	Scalable SPARQL Querying of Large RDF Graphs	2011	VLDB	0.00017413839

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