The Power of Summarization in Graph Mining and Learning: Smaller Data, Faster Methods, More Interpretability
Summary: A survey of graph summarization as a unifying strategy for scalable mining and learning: compact knowledge graphs enable refinement/on-device queries, graph streams reveal persistent activity, and GNN summaries improve speed and interpretability. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Danai Koutra (University of Michigan)
BibTeX Citation
@article{koutra_vldb21,
title = {{The Power of Summarization in Graph Mining and Learning: Smaller Data, Faster Methods, More Interpretability}},
author = {Koutra, Danai},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {13},
pages = {3416--3416},
doi = {10.14778/3484224.3484238},
url = {https://doi.org/10.14778/3484224.3484238},
year = {2021}
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