Implementation of "On-Accelerating-Multi-Layered-Heterogeneous-Network-Embedding-Learning"

• numpy
• scipy

(may have to be independently installed) or pip install -r requirements.txt to install all dependencies

1. cd deepwalk
2. pip install -r requirements.txt
3. python setup.py install

• Example Usage

  $deepwalk --input example_graphs/karate.adjlist --number-walks 10 --representation-size 128 --walk-length 3 --window-size 2 --output karate.embeddings

--input: input_filename

1. --format adjlist for an adjacency list, e.g:

   1 2 3 4 5 6 7 8 9 11 12 13 14 18 20 22 32
   2 1 3 4 8 14 18 20 22 31
   3 1 2 4 8 9 10 14 28 29 33
   ...
   

2. --format edgelist for an edge list, e.g:

   1 2
   1 3
   1 4
   ...
   

3. • --format mat for a Matlab .mat file containing an adjacency matrix

     (note, you must also specify the variable name of the adjacency matrix --matfile-variable-name)

--output: output_filename

The output representations in skipgram format - first line is header, all other lines are node-id and d dimensional representation:

34 64
1 0.016579 -0.033659 0.342167 -0.046998 ...
2 -0.007003 0.265891 -0.351422 0.043923 ...
...

For Politics data:

deepwalk --format edgelist --input Politics-UK/lists-to-community.txt --number-walks 10 --representation-size 128 --walk-length 3 --window-size 2 --output Politics-UK/lists-to-community.embeddings

For Olympics data:

deepwalk --format edgelist --input Olympics/lists-to-community.txt --number-walks 10 --representation-size 128 --walk-length 3 --window-size 2 --output Olympics/lists-to-community.embeddings