GPU-based Compressed Graph Traversal (GCGT)

Source code for the paper:

GPU-based Graph Traversal on Compressed Graphs

Datasets

Due to the capacity limitation, please obtain the datasets used in the paper from their original sources:

• ljournal-2008: http://law.di.unimi.it/webdata/ljournal-2008/
• uk-2002: http://law.di.unimi.it/webdata/uk-2002/
• brain: http://networkrepository.com/bn-human-Jung2015-M87113878.php
• twitter: http://an.kaist.ac.kr/traces/WWW2010.html
• uk-2007: http://law.di.unimi.it/webdata/uk-2007-05/

Baselines

• Ligra and Ligra+: https://github.com/jshun/ligra
• Gunrock: https://github.com/gunrock/gunrock

Reordering

• BFSOrder and LLP: http://law.di.unimi.it/software.php
• Gorder: https://github.com/datourat/Gorder

Virtual Node Miner

Our third-party implementation of the Virtual Node Miner in A Scalable Pattern Mining Approach to Web Graph Compression with Communities. Please refer to virtual_node_miner.

Compressed Graph Representation (CGR) Compressor

The Compressed Graph Representation (CGR) Compressor. Please refer to cgr_compressor.

BFS-GCGT

The CUDA implementation of BFS based on the proposed GCGT. Please refer to bfs_gcgt.

Reference

@inproceedings{DBLP:conf/sigmod/Sha19GCGT,
  author    = {Mo Sha and
               Yuchen Li and
               Kian{-}Lee Tan},
  title     = {GPU-based Graph Traversal on Compressed Graphs},
  booktitle = {{SIGMOD} Conference},
  pages     = {775--792},
  publisher = {{ACM}},
  year      = {2019}
}

License

MIT