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Fast Iterative Graph Computation with Block Updates

Summary: Block-oriented computation model to scale iterative graph processing with light per-vertex workloads, mitigating memory-wall bottlenecks. A block-aware runtime preserves vertex-centric APIs while boosting cache efficiency and achieving notable speedups on large graphs. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10686
Venue
VLDB
Year
2013
Pagerank
0.0001091808
Overall Rank
1,685 | 88.28%
DOI
-

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

Rank Cited Paper Year Venue Pagerank
4 Pregel: A System for Large-Scale Graph Processing 2010 SIGMOD 0.0019005923
23 A Critique of ANSI SQL Isolation Levels 1995 SIGMOD 0.00083894938
37 Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud 2012 VLDB 0.0007522744
522 Differential dataflow 2013 CIDR 0.00021099241
1,452 Asynchronous Large-Scale Graph Processing Made Easy 2013 CIDR 0.00011919499
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