node2vec_linkprediction

Testing link prediction using Node2Vec

Installation

Requirements:

• git
• Python 2.7
• gensim
• networkx
• numpy
• matplotlib
• scikit-learn
• node2vec

To install using Anaconda:

1. To install on Mac OS or Linux, download and install Anaconda (2 or 3) from the following website: https://www.continuum.io/downloads

2. At a command prompt, create a python 2.7 environment and install required packages:

   conda create -n py27 python=2.7 numpy ipython matplotlib seaborn networkx gensim scikit-learn

3. Switch to this environment:

   source activate py27

4. Get node2vec python code:

   git clone https://github.com/aditya-grover/node2vec.git

5. Copy node2vec.py to link prediction code directory:

   cp node2vec/src/node2vec.py <node2vec_linkprediction path>

Usage

To use the link_prediction code, we assume the graph data is saved in the form of an edgelist of node pairs on a seperate line:

Example edgelist:
1 2
3 4
4 2

A task must be specified, which is one of:

• edgeencoding: Test the node2vec embedding using different edge functions, and analyse their performance.

• sensitivity: Run a parameter sensitivity test on the node2vec parameters of q, p, r, l, d, and k.

• gridsearch: Run a grid search on the node2vec parameters of q, p.

For example, to test the edge encodings for the graph AstroPh.edgelist, with averaging over five random walk samplings in node2vec:

python link_prediction.py edgeembedding --input AstroPh.edgelist  --num_experiments 5

For help on the options, use:

python link_prediction.py --help

The default values for the experiments and parameter search settings are in the code link_prediction.py.