A parallel implementation of “graph2vec: Learning Distributed Representations of Graphs” (MLGWorkshop 2017).

graph2vec

Abstract

The model is now also available in the package Karate Club.

This repository provides an implementation for graph2vec as it is described in:

graph2vec: Learning distributed representations of graphs. Narayanan, Annamalai and Chandramohan, Mahinthan and Venkatesan, Rajasekar and Chen, Lihui and Liu, Yang MLG 2017, 13th International Workshop on Mining and Learning with Graphs (MLGWorkshop 2017).

The original TensorFlow implementation is available [here].

Requirements

The codebase is implemented in Python 3.5.2 | Anaconda 4.2.0 (64-bit). Package versions used for development are just below.

jsonschema        2.6.0
tqdm              4.28.1
numpy             1.15.4
pandas            0.23.4
texttable         1.5.0
gensim            3.6.0
networkx          2.4
joblib            0.13.0
logging           0.4.9.6  

Datasets

Options

Learning of the embedding is handled by the src/graph2vec.py script which provides the following command line arguments.

Input and output options

  --input-path   STR    Input folder.           Default is `dataset/`.
  --output-path  STR    Embeddings path.        Default is `features/nci1.csv`.

Model options

  --dimensions     INT          Number of dimensions.                             Default is 128.
  --workers        INT          Number of workers.                                Default is 4.
  --epochs         INT          Number of training epochs.                        Default is 1.
  --min-count      INT          Minimal feature count to keep.                    Default is 5.
  --wl-iterations  INT          Number of feature extraction recursions.          Default is 2.
  --learning-rate  FLOAT        Initial learning rate.                            Default is 0.025.
  --down-sampling  FLOAT        Down sampling rate for frequent features.         Default is 0.0001.

Examples

$ python src/graph2vec.py

Creating an embedding of an other dataset. Saving the output in a custom place.

$ python src/graph2vec.py --input-path new_data/ --output-path features/nci2.csv

Creating an embedding of the default dataset in 32 dimensions.

$ python src/graph2vec.py --dimensions 32