Social Network Analyzer

This is a simple network analysis app, created for the purpose of evaluating embedding algorithms, but can also be used to perform standard graph analysis techniques. Currently implemented algorithms and techniques:

• Embedding algorithms:
  • node2vec
    • node2vec_custom
    • node2vec_snap
    • node2vec_eliorc
  • DeepWalk
    • deepwalk_custom
    • deepwalk_phanein
• Machine learning techniques:
  • Link prediction
  • Node classification
  • K-means clustering
• Community detection:
  • Girvan-Newman
  • Label propagation

Prerequisites

You can install the Python requirements for this project by running:

pip install -r requirements.txt

How to run the program

Because of specific Python requirements for multiple packages it's advised to run the program using Docker Compose:

docker-compose build
docker-compose up

To run a specific embedding algorithm with defined arguments:

python main.py --input git/git.gpickle \
               --output git/git.embedding \
               --results git/link-prediction/git_deepwalk_custom_logisticalregression.csv \
               --method deepwalk_custom \
               --classifier logisticalregression \
               --evaluation link-prediction \

To start multiple embedding algorithms in a row with defined arguments edit and run the scripts int the /scripts directory:

python /scripts/batch_link_prediction.py

You can find more examples on how to start the program in example_commands.md.

Application arguments

• --input: Input graph in .gpickle format in the /input directory. Argument required.
• --output: Filename to save the graph embeddings in the /embeddings directory. Argument required.
• --results: Filename to save the evaluation results in the /results directory. Argument required.
• --dimensions: Dimensionality of the word vectors. Default: 128
• --walk-length: The number of nodes in each walk. Default: 64
• --num-walks: Number of walks from each node. Default: 32
• --p: The node2vec return parameter p. Default: 2
• --q: The node2vec in-out parameter q. Default: 1
• --workers: Number of worker threads to train the model. Default: 1
• --seed: A seed for the random number generator. Default: 0
• --test-percentage: Percentage of graph edges that should be used for testing classifiers. Default: 0.1
• --train-percentage: Percentage of graph edges that should be used for training classifiers. Default: 0.1
• --window-size: Maximum distance between the current and predicted word within a sentence. Default: 10
• --weighted: Denotes if the graph is weighted. Default: False
• --directed: Denotes if the graph is directed. Default: False
• --iter: Number of iterations (epochs) over the corpus. Default: 1
• --method: The graph embedding algorithm and specific implementation. Choices: node2vec_snap, node2vec_eliorc, node2vec_custom, deepwalk_phanein and deepwalk_custom. Argument required.
• --community-method: The community detection method for calculating the number of clusters. Choices: girvan_newman_custom, label_propagation_custom, girvan_newman_nx and label_propagation_nx.
• --evaluation: The social network analysis technique to be used. Choices: link, randomforest and gradientboost. Argument required.
• --classifier: The classifier for evaluation. Choices: logisticalregression, randomforest and gradientboost. Argument required.
• --embed: Denotes if the embedding should be calculated or loaded from an existing file. Default: False
• --node-ml-target: The node target label for classification. Default: 'ml_target'
• --k: Number of node samples to estimate betweenness. Default: None
• --converging: Iteration when to cut off the Girvan-Newman algorithm if modularity is decreasing. Default: 10
• --visuals: Denotes if the application should plot figures. Default: True

Structure

• docs/: Sphinx generated documentation of the application.
• embeddings/: Node embeddings saved in CSV format.
• input/: Graph network input files in .gpickle format.
• results/: Results of evaluation techniques if such were performed.
• scripts/: Python scripts for batch task executions.
• src/: Source code of the application.