# Graph Summarization Methods and Applications: A Survey

arXiv: https://arxiv.org/abs/1612.04883 · HTML: https://mlsanity.com/paper/1612.04883/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2016.

## Built on by (most cited first, 16 of 16)

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- [APEX2: Adaptive and Extreme Summarization for Personalized Knowledge Graphs](https://mlsanity.com/paper/2412.17336/) (2024)
- [Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training](https://mlsanity.com/paper/2312.15520/) (2023)
- [GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra](https://mlsanity.com/paper/2103.03653/) (2021)
- [Scaling R-GCN Training with Graph Summarization](https://mlsanity.com/paper/2203.02622/) (2022)
- [Discovering Representative Attribute-stars via Minimum Description Length](https://mlsanity.com/paper/2204.12704/) (2022)
- [Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening](https://mlsanity.com/paper/2505.14033/) (2025)
- [Leveraging the Graph Structure of Neural Network Training Dynamics](https://mlsanity.com/paper/2111.05410/) (2021)
- [Differentially Describing Groups of Graphs](https://mlsanity.com/paper/2201.04064/) (2021)
- [Large-scale multi-objective influence maximisation with network downscaling](https://mlsanity.com/paper/2204.06250/) (2022)
- [Promise and Limitations of Supervised Optimal Transport-Based Graph Summarization via Information Theoretic Measures](https://mlsanity.com/paper/2305.07138/) (2023)
- [Knowledge Graph Pruning for Recommendation](https://mlsanity.com/paper/2405.11531/) (2024)
- [Balancing Summarization and Change Detection in Graph Streams](https://mlsanity.com/paper/2311.18694/) (2023)
- [CGS: Configurable Graph Summarization with Bounded Neighborhood Loss and Query Support](https://mlsanity.com/paper/2607.10969/) (2026)
- [Aggregating User Preferences while Ensuring Equity, Diversity, and Inclusion using Graph Summarization](https://mlsanity.com/paper/2610.07128/) (2026)

## Abstract (arXiv)

While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind, and the challenges of, graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.