SLUGGER: Lossless Hierarchical Summarization of Massive Graphs

Overview

SLUGGER: Lossless Hierarchical Summarization of Massive Graphs

Source code for the paper SLUGGER: Lossless Hierarchical Summarization of Massive Graphs, Kyuhan Lee*, Jihoon Ko*, and Kijung Shin, ICDE 2022.

SLUGGER (Scalable Lossless Summarization of Graphs with Hierarchy) is a lossless hierarchical graph summarization algorithm that has the following superiorities:

  • Effective: yielding up to 29.6% more concise summaries than its state-of-the-art competitors.
  • Fast: summarizing a graph with 0.8 billion edges in a few hours.
  • Scalable: scaling linear with the number of edges in the input graph.

Building and Running SLUGGER

Please see User Guide.

Running Demo

For demo, please type 'make.'

Datasets and Contributors

The datasets used in the paper are provided here.

Terms and Conditions

If you use this code as a part of any published research, please acknowledge our ICDE 2022 paper.

Contact Information

If you have any questions, please contact Kyuhan Lee.

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Owner
Graduate School of AI, KAIST
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