This is a Python3 implementation of Stanford University's node2vec model
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Compute transition probabilities for all the nodes. (2nd order Markov chain)
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Generate biased walks based on probabilities
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Generate embeddings with SGD
Install pre-reqs by running the following command:
pip3 install -r req.txt
To run node2vec with default arguments, execute the following command from the home directory:
python3 src/main.py
A full list of command line arguments are shown by entering:
python3 src/main.py -h
usage: main.py [-h] [--input INPUT] [--output OUTPUT] [--p P] [--q Q]
[--walks WALKS] [--length LENGTH] [--d D] [--window WINDOW]
[--workers WORKERS] [--directed]
node2vec implementation
optional arguments:
-h, --help show this help message and exit
--input INPUT Path for input edgelist
--output OUTPUT Path for saving output embeddings
--p P Return parameter
--q Q In-out parameter
--walks WALKS Walks per node
--length LENGTH Length of each walk
--d D Dimension of output embeddings
--window WINDOW Window size for word2vec
--workers WORKERS Number of workers to assign for random walk and word2vec
--directed Flad to specify if graph is directed. Default is undirected.
Note: Zachary's Karate club network is used by default if no argument is provided for the input flag. Do check the default values in main.py
To generate embeddings for Zachary's Karate club network with custom arguments, the following can be used
python3 src/main.py --p 0.4 --q 1 --walks 20 --length 80 --d 256
References:
node2vec: Scalable Feature Learning for Networks - Aditya Grover, Jure Leskovec
Contact me at [email protected]
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