GEM is a Python module that implements many graph (a.k.a. network) embedding algorithms. GEM is distributed under BSD license.
The module was developed and is maintained by Palash Goyal.
GEM implements the following graph embedding techniques:
- Laplacian Eigenmaps
- Locally Linear Embedding
- Graph Factorization
- Higher-Prder Proximity preserved Embedding (HOPE)
- Structural Deep Network Embedding (SDNE)
- node2vec
A survey of these methods can be found in Graph Embedding Techniques, Applications, and Performance: A Survey.
We store all graphs using the DiGraph as directed weighted graph in python package networkx. The weight of an edge is stored as attribute "weight". We save each edge in undirected graph as two directed edges.
The graphs are saved using nx.write_gpickle in the networkx format and can be loaded by using nx.read_gpickle.
- gem/embedding: existing approaches for graph embedding, where each method is a separate file
- gem/evaluation: evaluation tasks for graph embedding, including graph reconstruction, link prediction, node classification and visualization
- gem/utils: utility functions for graph manipulation, evaluation and etc.
- gem/data: input test graph (currently has Zachary's Karate graph)
- gem/c_src: source files for methods implemented in C++
- gem/c_ext: Python interface for source files in c_src using Boost.Python
GEM is tested to work on Python 2.7.
The required dependencies are: Numpy >= 1.12.0, SciPy >= 0.19.0, Networkx >= 1.11, Scikit-learn >= 0.18.1.
To run SDNE, GEM requires Theano >= 0.9.0 and Keras = 2.0.2.
The package uses setuptools, which is a common way of installing python modules. To install in your home directory, use:
python setup.py install --user
To install for all users on Unix/Linux:
sudo python setup.py install
Run Graph Factorization on Karate graph and evaluate it on graph reconstruction:
from gem.embedding.gf import GraphFactorization as gf
from gem.evaluation import evaluate_graph_reconstruction as gr
from gem.utils import graph_util
# Instatiate the embedding method with hyperparameters
em = gf(2, 100000, 1*10**-4, 1.0)
# Load graph
graph = graph_util.loadGraphFromEdgeListTxt('gem/data/karate.edgelist')
# Learn embedding - accepts a networkx graph or file with edge list
Y, t = em.learn_embedding(graph, edge_f=None, is_weighted=True, no_python=True)
# Evaluate on graph reconstruction
MAP, prec_curv = gr.evaluateStaticGraphReconstruction(graph, em, Y, None)
@article{goyal2017graph,
title={Graph Embedding Techniques, Applications, and Performance: A Survey},
author={Goyal, Palash and Ferrara, Emilio},
journal={arXiv preprint arXiv:1705.02801},
year={2017}
}