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 |
|---|---|---|---|---|
| 387 | Graph Summarization with Bounded Error | 2008 | SIGMOD | 0.00024682268 |
| 435 | Efficient Aggregation for Graph Summarization | 2008 | SIGMOD | 0.00023268266 |
| 784 | Efficiently Answering Reachability Queries on Very Large Directed Graphs | 2008 | SIGMOD | 0.00016648392 |
| 1,572 | Query Preserving Graph Compression | 2012 | SIGMOD | 0.00011296109 |
| 2,054 | Graph Cube: On Warehousing and OLAP Multidimensional Networks | 2011 | SIGMOD | 9.682102e-05 |
| 2,609 | Graph Stream Summarization: From Big Bang to Big Crunch | 2016 | SIGMOD | 8.4587236e-05 |
| 4,758 | Efficient Graph Summarization using Weighted LSH at Billion-Scale | 2021 | SIGMOD | 5.9347454e-05 |
| 5,030 | Making Graphs Compact by Lossless Contraction | 2021 | SIGMOD | 5.7445683e-05 |
| 5,975 | Summarizing Static and Dynamic Big Graphs | 2017 | VLDB | 5.2452824e-05 |
| 6,330 | Utility-Driven Graph Summarization | 2019 | VLDB | 5.1028624e-05 |
| 7,179 | A Hierarchical Contraction Scheme for Querying Big Graphs | 2022 | SIGMOD | 4.803776e-05 |
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