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NScale: Neighborhood-centric Analytics on Large Graphs

Summary: NSCALE enables neighborhood-centric analytics on large graphs, programming at the subgraph level instead of per-vertex. GEL extraction/loading, YARN-based distribution, and overlap-aware execution minimize memory and I/O, delivering orders-of-magnitude gains. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10848
Venue
VLDB
Year
2014
Pagerank
4.4763421e-05
Overall Rank
8,648 | 39.84%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
5,017 TurboGraph++: A Scalable and Fast Graph Analytics System 2018 SIGMOD 5.7574792e-05
5,570 iTurboGraph: Scaling and Automating Incremental Graph Analytics 2021 SIGMOD 5.4284968e-05
6,709 Big Graph Analytics Systems 2016 SIGMOD 4.9529145e-05
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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
37 Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud 2012 VLDB 0.0007522744
574 From "Think Like a Vertex" to "Think Like a Graph" 2014 VLDB 0.00019883211
582 Scalable SPARQL Querying of Large RDF Graphs 2011 VLDB 0.00019723083
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