jwalk 0.5.0

Representational learning on graphs

jwalk performs random walks on a graph and learns representations for nodes using Word2Vec. It also has options to train existing models online and specify weights.

Install

pip install -U jwalk

Build

make build

Usage

jwalk -i tests/data/karate.edgelist -o karate.emb --delimiter=' '

To see the full list of options:

jwalk --help

Prompt parameters:
  debug:            drop a debugger if an exception is raised
  delimiter:        delimiter for input file
  embedding-size:   dimension of word2vec embedding (default=200)
  has-header:       boolean if csv has header row
  help (-h):        argparse help
  input (-i):       file input (edge list or scipy adjacency CSR matrix)
  log-level (-l)    logging level (default=INFO)
  model (-m):       use a pre-existing model
  num-walks (-n):   number of of random walks per graph (default=1)
  output (-o):      file output
  stats:            boolean to calculate walk statistics [requires pandas]
  undirected:       make graph undirected
  walk-length:      length of random walks (default=10)
  window-size:      word2vec window size (default=5)
  workers:          number of workers (default=multiprocessing.cpu_count)

Test

Running unit tests:

make test

Running linter:

make lint

Running tox:

make test-all

License

Apache License 2.0

References

• [paper]: arXiv:1403.6652 [cs.SI] “DeepWalk: Online Learning of Social Representations”

• [paper]: arXiv:1607.00653 [cs.SI] “node2vec: Scalable Feature Learning for Networks”