MOCgraph: Scalable Distributed Graph Processing Using Message Online Computing
Summary: MOCgraph extends Giraph with message online computing, streaming incoming messages to bound memory use despite large intermediate results. It also enables efficient out-of-core graph analytics, improving scalability when in-memory distributed frameworks exhaust RAM. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Chang Zhou (Peking University)
- 2. Jun Gao (Peking University)
- 3. Binbin Sun (Huawei)
- 4. Jeffrey Xu Yu (Chinese University of Hong Kong)
BibTeX Citation
@article{zhou_vldb15,
title = {{MOCgraph: Scalable Distributed Graph Processing Using Message Online Computing}},
author = {Zhou, Chang and Gao, Jun and Sun, Binbin and Yu, Jeffrey Xu},
journal = {PVLDB},
series = {{VLDB} '15},
volume = {8},
number = {4},
pages = {377--388},
doi = {10.14778/2735496.2735501},
url = {https://doi.org/10.14778/2735496.2735501},
year = {2015}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,085 | Weaver: A High-Performance, Transactional Graph Database Based on Refinable Timestamps | 2016 | VLDB | 7.7709242e-05 |
| 3,303 | A Distributed Multi-GPU System for Fast Graph Processing | 2018 | VLDB | 7.5405596e-05 |
| 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 |
| 9,424 | Hybrid Pulling/Pushing for I/O-Efficient Distributed and Iterative Graph Computing | 2016 | SIGMOD | 5.2715528e-05 |
| 11,963 | My Weak Consistency is Strong: When Bad Things Do Not Come in Threes | 2017 | CIDR | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3 | Pregel: A System for Large-Scale Graph Processing | 2010 | SIGMOD | 0.0012250108 |
| 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 |
| 1,300 | An Experimental Comparison of Pregel-like Graph Processing Systems | 2014 | VLDB | 0.00011258552 |
| 1,803 | Towards Effective Partition Management for Large Graphs | 2012 | SIGMOD | 9.724402e-05 |
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|---|---|---|---|---|
| 1 | 2,221 | Streaming Graph Partitioning: An Experimental Study | 2018 | VLDB |
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| 5 | 389 | One Trillion Edges: Graph Processing at Facebook-Scale | 2015 | VLDB |
| 6 | 7,447 | Experimental Analysis of Distributed Graph Systems | 2018 | VLDB |
| 7 | 487 | From "Think Like a Vertex" to "Think Like a Graph" | 2014 | VLDB |
| 8 | 1,911 | Fast Iterative Graph Computation with Block Updates | 2013 | VLDB |
| 9 | 3,008 | Scalable Big Graph Processing in MapReduce | 2014 | SIGMOD |
| 10 | 1,875 | Large-Scale Distributed Graph Computing Systems: An Experimental Evaluation | 2015 | VLDB |