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APyTorch implementation ofSplitter: Learning Node Representations that Capture Multiple Social Contexts (WWW 2019).

Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph. But can nodes really be best described by a single vector representation? In this work, we propose a method for learning multiple representations of the nodes in a graph (e.g., the users of a social network). Based on a principled decomposition of the ego-network, each representation encodes the role of the node in a different local community in which the nodes participate. These representations allow for improved reconstruction of the nuanced relationships that occur in the graph a phenomenon that we illustrate through state-of-the-art results on link prediction tasks on a variety of graphs, reducing the error by up to 90%. In addition, we show that these embeddings allow for effective visual analysis of the learned community structure.

This repository provides a PyTorch implementation of Splitter as described in the paper:

Splitter: Learning Node Representations that Capture Multiple Social Contexts.Alessandro Epasto and Bryan Perozzi.WWW, 2019.[Paper]

The original Tensorflow implementation is available[here].

The codebase is implemented in Python 3.5.2. package versions used for development are just below.

networkx          1.11tqdm              4.28.1numpy             1.15.4pandas            0.23.4texttable         1.5.0scipy             1.1.0argparse          1.1.0torch             1.1.0gensim            3.6.0

The code takes the **edge list** of the graph in a csv file. Every row indicates an edge between two nodes separated by a comma. The first row is a header. Nodes should be indexed starting with 0. A sample graph for `Cora` is included in the `input/` directory.

The embeddings are saved in the `input/` directory. Each embedding has a header and a column with the node IDs. Finally, the node embedding is sorted by the node ID column.

The training of a Splitter embedding is handled by the `src/main.py` script which provides the following command line arguments.

  --edge-path               STR    Edge list csv.           Default is `input/chameleon_edges.csv`.  --embedding-output-path   STR    Embedding output csv.    Default is `output/chameleon_embedding.csv`.  --persona-output-path     STR    Persona mapping JSON.    Default is `output/chameleon_personas.json`.

  --seed               INT     Random seed.                       Default is 42.  --number of walks    INT     Number of random walks per node.   Default is 10.  --window-size        INT     Skip-gram window size.             Default is 5.  --negative-samples   INT     Number of negative samples.        Default is 5.  --walk-length        INT     Random walk length.                Default is 40.  --lambd              FLOAT   Regularization parameter.          Default is 0.1  --dimensions         INT     Number of embedding dimensions.    Default is 128.  --workers            INT     Number of cores for pre-training.  Default is 4.     --learning-rate      FLOAT   SGD learning rate.                 Default is 0.025

The following commands learn an embedding and save it with the persona map. Training a model on the default dataset.

python src/main.py

Training a Splitter model with 32 dimensions.

python src/main.py --dimensions 32

Increasing the number of walks and the walk length.

python src/main.py --number-of-walks 20 --walk-length 80

License

• GNU License