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Navigating the Maze of Graph Analytics Frameworks using Massive Graph Datasets

Summary: Comparative study of graph analytics frameworks (GraphLab, CombBLAS, Giraph, SociaLite, Galois) vs hand-optimized baselines on massive sparse graphs. Pinpoints bottlenecks across algorithms, models, and runtimes; outlines pragmatic changes to shrink the ninja gap and guide framework choice by ease of use. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4951
Venue
SIGMOD
Year
2014
Pagerank
0.00010104703
Overall Rank
1,654 | 88.66%
DOI
10.1145/2588555.2610518

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{satish_sigmod14,
        title = {{Navigating the Maze of Graph Analytics Frameworks using Massive Graph Datasets}},
        author = {Satish, Nadathur and Sundaram, Narayanan and Patwary, Md. Mostofa Ali and Seo, Jiwon and Park, Jongsoo and Hassaan, M. Amber and Sengupta, Shubho and Yin, Zhaoming and Dubey, Pradeep},
        series = {{SIGMOD} '14},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2588555.2610518},
        url = {https://dl.acm.org/doi/10.1145/2588555.2610518},
        year = {2014}
}

Incoming Citations (Sorted by Pagerank)

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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
1,539 Distributed SociaLite: A Datalog-Based Language for Large-Scale Graph Analysis 2013 VLDB 0.000104329
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