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)
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Authors
- 1. Jiyang Bai
- 2. Peixiang Zhao
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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 |
|---|---|---|---|---|
| 388 | Graph Summarization with Bounded Error | 2008 | SIGMOD | 0.00024662272 |
| 435 | Efficient Aggregation for Graph Summarization | 2008 | SIGMOD | 0.00023260172 |
| 788 | Efficiently Answering Reachability Queries on Very Large Directed Graphs | 2008 | SIGMOD | 0.00016650034 |
| 1,579 | Query Preserving Graph Compression | 2012 | SIGMOD | 0.00011283792 |
| 2,048 | Graph Cube: On Warehousing and OLAP Multidimensional Networks | 2011 | SIGMOD | 9.6914395e-05 |
| 2,607 | Graph Stream Summarization: From Big Bang to Big Crunch | 2016 | SIGMOD | 8.4630211e-05 |
| 4,761 | Efficient Graph Summarization using Weighted LSH at Billion-Scale | 2021 | SIGMOD | 5.9404527e-05 |
| 4,836 | Making Graphs Compact by Lossless Contraction | 2021 | SIGMOD | 5.8896897e-05 |
| 5,968 | Summarizing Static and Dynamic Big Graphs | 2017 | VLDB | 5.2503253e-05 |
| 6,329 | Utility-Driven Graph Summarization | 2019 | VLDB | 5.1077685e-05 |
| 6,730 | A Hierarchical Contraction Scheme for Querying Big Graphs | 2022 | SIGMOD | 4.9479867e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 388 | Graph Summarization with Bounded Error | 2008 | SIGMOD | 0.00024662272 |
| 6,210 | Summarizing Answer Graphs Induced by Keyword Queries | 2013 | VLDB | 5.1560547e-05 |
| 3,593 | Graph-Based Synopses for Relational Selectivity Estimation | 2006 | SIGMOD | 6.9385476e-05 |
| 2,607 | Graph Stream Summarization: From Big Bang to Big Crunch | 2016 | SIGMOD | 8.4630211e-05 |
| 13,265 | The Power of Summarization in Graph Mining and Learning: Smaller Data, Faster Methods, More Interpretability | 2021 | VLDB | - |
| 4,761 | Efficient Graph Summarization using Weighted LSH at Billion-Scale | 2021 | SIGMOD | 5.9404527e-05 |
| 10,964 | Graph Summarization: Compactness Meets Efficiency | 2024 | SIGMOD | 4.1945683e-05 |
| 5,968 | Summarizing Static and Dynamic Big Graphs | 2017 | VLDB | 5.2503253e-05 |
| 435 | Efficient Aggregation for Graph Summarization | 2008 | SIGMOD | 0.00023260172 |
| 6,329 | Utility-Driven Graph Summarization | 2019 | VLDB | 5.1077685e-05 |