Poligras: Policy-based Graph Summarization
Summary: Poligras uses a learned probabilistic policy (neural networks) to model and optimize the core supernode-pair selection/merging step in graph summarization. First scalable, learning-enhanced method producing lossless supergraph+correction summaries with much better quality/runtime than prior SOTA on large real graphs. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Jiyang Bai (Florida State University)
- 2. Peixiang Zhao (Florida State University)
BibTeX Citation
@article{bai_vldb24,
title = {{Poligras: Policy-based Graph Summarization}},
author = {Bai, Jiyang and Zhao, Peixiang},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {10},
pages = {2432--2444},
doi = {10.14778/3675034.3675037},
url = {https://doi.org/10.14778/3675034.3675037},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 497 | Efficient Aggregation for Graph Summarization | 2008 | SIGMOD | 0.00017318153 |
| 563 | Graph Summarization with Bounded Error | 2008 | SIGMOD | 0.00016327158 |
| 774 | Efficiently Answering Reachability Queries on Very Large Directed Graphs | 2008 | SIGMOD | 0.00014083518 |
| 1,375 | Query Preserving Graph Compression | 2012 | SIGMOD | 0.00010877488 |
| 2,186 | Graph Stream Summarization: From Big Bang to Big Crunch | 2016 | SIGMOD | 8.8916948e-05 |
| 3,222 | Graph Cube: On Warehousing and OLAP Multidimensional Networks | 2011 | SIGMOD | 7.5135441e-05 |
| 5,075 | Efficient Graph Summarization using Weighted LSH at Billion-Scale | 2021 | SIGMOD | 6.2860889e-05 |
| 5,554 | Making Graphs Compact by Lossless Contraction | 2021 | SIGMOD | 6.0858703e-05 |
| 5,660 | Summarizing Static and Dynamic Big Graphs | 2017 | VLDB | 6.0478621e-05 |
| 6,388 | Utility-Driven Graph Summarization | 2019 | VLDB | 5.8023555e-05 |
| 7,015 | A Hierarchical Contraction Scheme for Querying Big Graphs | 2022 | SIGMOD | 5.6210836e-05 |
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