Skip to content

Latest commit

 

History

History
99 lines (67 loc) · 3.09 KB

README.md

File metadata and controls

99 lines (67 loc) · 3.09 KB
Raw

This codebase gives the reference implementation of DistGER as described in the paper:

Distributed Graph Embedding with Information-Oriented Random Walks.
Peng Fang, Arijit Khan, Siqiang Luo, Fang Wang, Dan Feng, Zhenli Li, Wei Yin and Yuchao Cao
Published to 49th International Conference on Very Large Data Bases (VLDB 2023)

Prerequisites

Datasets

The evaluated dataset Youtube and LiveJournal are prepraed in the "dataset" directory.

Since the the space limited of the repository, the other datasets Twitter, Com-Orkut and Flickr can be found in their open resource.

Setup

First Compile DistGER with CMake:

mkdir build && cd build

cmake ..

make

Then the compiled application executable files are installed at the "bin" directory:

ls ./bin

Partitioning

If we need to run the train data for the downstream tasks, such as Link prediction, the test data also should be processed.

cd build

./bin/mpgp -i [train_data] -e [test_data] -v [vertex_num] -p [partition_num] -t [int:0, float:1]

The partitioned dataset will be saved in the input dataset directory.

Graph Embedding

To start the embedding, we fist need to cover the train graph to binary format

cd build

./bin/gconverter -i ../dataset/LJ.train-8-r -o ./LJ-8.data-r -s weighted

Then create the "out" directory to save the walks file or embedding file

mkdir out

Run in Single-machine Environment

mpiexec -n 8 ./bin/huge_walk -g ../dataset/LJ-8.data-r -p ../dataset/LJ-8.part -v 2238731 -w 2238731 --make-undirected -o ./out/walks.txt -eoutput ./out/LJ-r_emb.txt -size 128 -iter 1 -threads 72 -window 10 -negative 5  -batch-size 21 -min-count 0 -sample 1e-3 -alpha 0.01 -debug 2

Run in Distributed Environment

  • Copy the train dataset to the same path of each machine, or simply place it to a shared file system, such as NFS
  • Touch a host file to write each machine's IP address, such as ./hosts
  • Invoke the application with MPI
mpiexec -hostfile ./hosts -n 8 ./bin/huge_walk -g ../dataset/LJ-8.data-r -p ../dataset/LJ-8.part -v 2238731 -w 2238731 --make-undirected -o ./out/walks.txt -eoutput ./out/LJ-r_emb.txt -size 128 -iter 1 -threads 72 -window 10 -negative 5  -batch-size 21 -min-count 0 -sample 1e-3 -alpha 0.01 -debug 2

Check the output files in "out" directory

Citing

If you find DistGER useful for your research, please consider citing the following paper:

@article{fang2023distributed,
  title={Distributed Graph Embedding with Information-Oriented Random Walks},
  author={Fang, Peng and Khan, Arijit and Luo, Siqiang and Wang, Fang and Feng, Dan and Li, Zhenli and Yin, Wei and Cao, Yuchao},
  journal = {Proc. VLDB Endow.},
  year = {2023},
  volume = {16},
  number = {7},
  pages = {1643–1656}
}