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A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).
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Graph2Vec is an embedding algorithm which learns representations for a set of graphs using implicit factorization. The procedure places graphs in an abstract feature space where graphs with similar structural properties (Weisfehler-Lehman features) are clustered together. Graph2Vec has a linear runtime complexity in the number of graphs in the dataset which makes it extremely scalable. This specific implementation supports multi-core data processing in the feature extraction and factorization phases. (So far this is the only implementation which support multi-core processing in every phase).

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 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          1.11
joblib            0.13.0


The code takes an input folder with json files. Every file is a graph and files have a numeric index as a name. The json files have two keys. The first key called "edges" corresponds to the edge list of the graph. The second key "features" corresponds to the node features. If the second key is not present the WL machine defaults to use the node degree as a feature. A sample graph dataset from NCI1 is included in the dataset/ directory.


Learning of the embedding is handled by the src/ 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.


The following commands learn an embedding of the graphs and writes it to disk. The node representations are ordered by the ID.

Creating a graph2vec embedding of the default dataset with the default hyperparameter settings. Saving the embedding at the default path.

python src/

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

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

Creating an embedding of the default dataset in 32 dimensions.

python src/ --dimensions 32
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