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SSumM: Sparse Summarization of Massive Graphs

SSumM 최신버젼 다운로드

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# SSumM: Sparse Summarization of Massive Graphs
Source code for the paper [SSumM: Sparse Summarization of Massive Graphs](https://arxiv.org/abs/2006.01060), Kyuhan Lee*, Hyeonsoo Jo*, Jihoon Ko, Sungsu Lim, Kijung Shin, KDD 2020.

**SSumM** (**S**parse **Sum**marization of **M**assive Graphs) is a scalable and effective graph summarization algorithm that yields a sparse summary graph. Compared to its state-of-the-art competitors, **SSumM** has the following advantages:
* *Concise*: yields up to 11.2X smaller summary graphs with similar reconstruction error
* *Accurate*: achieves up to 4.2X smaller reconstruction error with similarly concise outputs
* *Scalable*: summarizies 26X larger graphs while exhibiting linear scalability

## Building and Running **SSumM**
Please see [User Guide](user_guide.pdf)

## Running Demo
For demo, please type 'make'

## Datasets and Contributors
The datasets used in the paper and authors information are listed [here](http://dmlab.kaist.ac.kr/ssumm/)

## Terms and Conditions
If you use this code as part of any published research, please acknowledge our KDD 2020 paper.

## Contact Information
If you have any questions, please contact [Kyuhan Lee](https://kyuhanlee.github.io/).

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