EvalNE: A Python library for evaluating Network Embedding methods on Link Prediction

This repository provides the source code for EvalNE, an open-source Python library designed for assessing and comparing the performance of Network Embedding (NE) methods on Link Prediction (LP) tasks. The library intends to simplify this complex and time consuming evaluation process by providing automation and abstraction of tasks such as hyper-parameter tuning, selection of train and test edges, negative sampling, selection of the scoring function, etc.

The library can be used both as a command line tool and an API. In its current version, EvalNE can evaluate unweighted directed and undirected simple networks.

The library is maintained by Alexandru Mara (alexandru.mara(at)ugent.be). The full documentation of EvalNE is hosted by Read the Docs and can be found here.

For Methodologists

A command line interface in combination with a configuration file allows the user to evaluate any publicly available implementation of a NE method without the need to write additional code. These implementations can be obtained from libraries such as OpenNE or GEM as well as directly from the web pages of the authors e.g. Deepwalk, Node2vec, LINE, PRUNE, Metapath2vec, CNE.

EvalNE also includes the following LP heuristics for both directed and undirected networks (in and out node neighbourhoods), which can be used as baselines:

• Random Prediction
• Common Neighbours
• Jaccard Coefficient
• Adamic Adar Index
• Preferential Attachment
• Resource Allocation Index

For practitioners

When used as an API, EvalNE provides functions to:

• Load and preprocess graphs
• Obtain general graph statistics
• Compute train/test/validation splits
• Generate false edges
• Evaluate link prediction from:
  • Node Embeddings
  • Edge Embeddings
  • Similarity scores (e.g. the ones given by LP heuristics)
• Provides functions that compute edge embeddings from node feature vectors
  • Average
  • Hadamard
  • Weighted L1
  • Weighted L2
• Any sklearn binary classifier can be used as a LP algorithm
• Implements several accuracy metrics.
• Includes parameter tuning subroutines

Instalation

The library has been tested on Python 2.7 and Python 3.6.

EvalNE depends on the following packages:

• Numpy
• Scipy
• Sklearn
• Matplotlib
• Networkx 2.2

Before installing EvalNE make sure that pip and python-tk packages are installed on your system, this can be done by running:

# Python 2
sudo apt-get install python-pip
sudo apt-get install python-tk

# Python 3
sudo apt-get install python3-pip
sudo apt-get install python3-tk

Option 1: Install the library using pip:

# Python 2
pip install evalne

# Python 3
pip3 install evalne

Option 2: Cloning the code and installing:

• Clone the EvalNE repository:

  git clone https://github.com/Dru-Mara/EvalNE.git
  cd EvalNE
  

• Download strict library dependencies and install:

  # Python 2
  pip install -r requirements.txt
  sudo python setup.py install
  
  # Python 3
  pip3 install -r requirements.txt
  sudo python3 setup.py install
  

Check the installation by running simple_example.py or functions_example.py e.g.:

# Python 2
cd examples/
python simple_example.py

# Python 3
cd examples/
python3 simple_example.py

NOTE: In order to run the evaluator_example.py script, the OpenNE library, PRUNE and Metapath2Vec are required. The instructions for installing them are available here, here, and here, respectively. The instructions on how to run evaluations using .ini files are provided in the next section.

Usage

As a command line tool

The library takes as input an .ini configuration file. This file allows the user to specify the evaluation settings, from the methods and baselines to be evaluated to the edge embedding methods, parameters to tune or scores to report.

An example conf.ini file is provided describing the available options for each parameter. This file can be either modified to simulate different evaluation settings or used as a template to generate other .ini files.

Additional configuration (.ini) files are provided replicating the experimental sections of different papers in the NE literature. These can be found in different folders under examples/. One such configuration file is examples/node2vec/conf_node2vec.ini. This file simulates the link prediction experiments of the paper "Scalable Feature Learning for Networks" by A. Grover and J. Leskovec.

Once the configuration is set, the evaluation can be run as indicated in the next subsection.

Running the conf examples

In order to run the evaluations using the provided conf.ini or any other .ini file, the following steps are necessary:

1. Download/Install the methods you want to test:

   • For conf.ini:
     • Install OpenNE
     • Install PRUNE
   • For other .ini files you may need:
     • Deepwalk, Node2vec, LINE, Metapath2vec, and/or CNE.

2. Download the datasets used in the examples:

   • For conf.ini:
     • StudentDB
     • ArXiv GR-QC
   • For other .ini files you may need:
     • Facebook (combined network)
     • Facebook-wallpost
     • ArXiv Astro-Ph
     • ArXiv Hep-Ph
     • BlogCatalog
     • Wikipedia
     • PPI

3. Set the correct dataset paths in the INPATHS option of the corresponding .ini file. And the correct method paths under METHODS_OPNE and/or METHODS_OTHER options.

4. Run the evaluation:

   # For conf.ini run:
   python evalne ./examples/conf.ini
   
   # For conf_node2vec.ini run:
   python evalne ./examples/node2vec/conf_node2vec.ini
   

Note: The input networks for EvalNE are required to be in edgelist format.

As an API

The library can be imported and used like any other Python module. Next we present a very basic example, for more complete ones we refer the user to the examples/ folder.

from evalne.evaluation import evaluator
from evalne.preprocessing import preprocess as pp

# Load and preprocess the network
G = pp.load_graph('../evalne/tests/data/network.edgelist')
G, _ = pp.prep_graph(G)

# Create an evaluator and generate train/test edge split
nee = evaluator.Evaluator()
_ = nee.traintest_split.compute_splits(G)

# Set the baselines
methods = ['random_prediction', 'common_neighbours', 'jaccard_coefficient']

# Evaluate baselines
nee.evaluate_baseline(methods=methods)

try:
    # Check if OpenNE is installed
    import openne

    # Set embedding methods from OpenNE
    methods = ['node2vec', 'deepwalk', 'GraRep']
    commands = [
        'python -m openne --method node2vec --graph-format edgelist --p 1 --q 1',
        'python -m openne --method deepWalk --graph-format edgelist --number-walks 40',
        'python -m openne --method grarep --graph-format edgelist --epochs 10']
    edge_emb = ['average', 'hadamard']

    # Evaluate embedding methods
    for i in range(len(methods)):
        command = commands[i] + " --input {} --output {} --representation-size {}"
        nee.evaluate_cmd(method_name=methods[i], method_type='ne', command=command,
                         edge_embedding_methods=edge_emb, input_delim=' ', output_delim=' ')

except ImportError:
    print("The OpenNE library is not installed. Reporting results only for the baselines...")
    pass

# Get output
results = nee.get_results()
for result in results:
    result.pretty_print()

Output

The library can provide two types of outputs, depending on the value of the SCORES option of the configuration file. If the keyword all is specified, the library will generate a file named eval_output.txt containing for each method and network analysed all the metrics available (auroc, precision, f-score, etc.). If more than one experiment repeat is requested the values reported will be the average over all the repeats. The output file will be located in the same path from which the evaluation was run.

Setting the SCORES option to %(maximize) will generate a similar output file as before. The content of this file, however, will be a table (Alg. x Networks) containing exclusively the score specified in the MAXIMIZE option for each combination of method and network averaged over all experiment repeats.

Additionally, if the option TRAINTEST_PATH contains a valid filename, EvalNE will create a file with that name under each of the OUTPATHS provided. In each of these paths the library will store the true and false train and test sets of edge.

NOTE: The tabular output is not available for mixes of directed and undirected networks.

Citation

If you have found EvaNE useful in your research, please cite our arXiv paper:

    @misc{Mara2019,
      author = {Alexandru Mara and Jefrey Lijffijt and Tijl De Bie},
      title = {EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction},
      year = {2019},
      archivePrefix = {arXiv},
      eprint = {1901.09691}
    }