SSumM: Sparse Summarization of Massive Graphs

Source code for the paper SSumM: Sparse Summarization of Massive Graphs, Kyuhan Lee*, Hyeonsoo Jo*, Jihoon Ko, Sungsu Lim, Kijung Shin, KDD 2020.

SSumM (Sparse Summarization of Massive 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

Running Demo

For demo, please type 'make'

Datasets and Contributors

The datasets used in the paper and authors information are listed here

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.