BiGraph

BiGraph is a Python package for Link prediction in bipartite networks.

Bug reports: https://github.com/bi-graph/bigraph/issues

Node based similarities and Katz has been implemented. you can find algorithms in bigraph module. Algorithms implemented so far:

Algorithms table

Number	Algorithm
1	`jaccard`
2	`adamic adar`
3	`common neighbors`
4	`preferential attachment`
5	`katz similarity`

Installation

Install the latest version of BiGraph:

$ pip install bigraph

Documentation

https://bigraph.readthedocs.io/en/latest/

Simple example

Predicting new links in a randomly generated graph using Adamic-Adar algorithm:

from bigraph.predict import aa_predict
from bigraph.preprocessing import import_files, make_graph


def adamic_adar_prediction():
    """
    Link prediction on bipartite networks
    :return: A dictionary containing predicted links
    """

    df, df_nodes = import_files()
    print(df)
    print(f"Graph Nodes: ", df_nodes)
    G = make_graph(df)
    print(G)
    predicted = aa_predict(G)  # Here we have called Adamic Adar method from bigraph module
    return predicted


# Executing the function

if __name__ == '__main__':
    adamic_adar_prediction()

Evaluating Adamic-Adar algorithm.
You can try other provided prediction algorithms by replacing the "aa" argument.

from bigraph.evaluation.evaluation import evaluate
from bigraph.preprocessing import import_files, make_graph


def adamic_adar_evaluation():
    """
    Evaluate Adamic-Adar algorithm using 10-Fold cross-validation 
    :return: A dictionary containing the evaluation results
    """
    df, df_nodes = import_files()
    G = make_graph(df)
    results = evaluate(G, k=10,
                       method='aa')  # Here we have evaluated adamic-adar
    # methods using evaluation module. Methods are 'jc', 'aa', 'pa', 'cn'
    return results


# Executing the function
if __name__ == '__main__':
    adamic_adar_evaluation()

Call for Contributions

The Bigraph project welcomes your expertise and enthusiasm!

Ways to contribute to Bigraph:

Writing code
Review pull requests
Develop tutorials, presentations, and other educational materials
Translate documentation and readme contents

Issues

If you happened to encounter any issue in the codes, please report it here. A better way is to fork the repository on Github and/or create a pull request.

Metrics

Metrics that are calculated during evaluation:

Metrics table

Number	Evaluattion metrics
1	`Precision`
2	`AUC`
3	`ROC`
4	`returns fpr*`
5	`returns tpr*`

For further usages and calculating different metrics

Dataset format

Your dataset should be in the following format (Exclude the 'Row' column):

Sample edges (links) dataset

Row	left_side	right_side	Weight*
1	`u0`	`v1`	1
2	`u2`	`v1`	1
3	`u1`	`v2`	1
4	`u3`	`v3`	1
5	`u4`	`v3`	2

Note that running
from bigraph.preprocessing import import_files
df, df_nodes = import_files()
will create a sample graph for you and will place it in the inputs directory.
Although the weight has not been involved in current version, but, the format will be the same.

More examples

Predicting new links in a randomly generated graph using following algorithms:

Preferential attachment
Jaccard similarity
Common neighbours

from bigraph.predict import pa_predict, jc_predict, cn_predict
from bigraph.preprocessing import import_files, make_graph


def main():
    """
    Link prediction on bipartite networks
    :return:
    """
    df, df_nodes = import_files()
    G = make_graph(df)
    pa_predict(G)  # Preferential attachment
    jc_predict(G)  # Jaccard coefficient
    cn_predict(G)  # Common neighbors


# Executing the function
if __name__ == '__main__':
    main()

References

References table

Number	Reference	Year
1	`Yang, Y., Lichtenwalter, R.N. & Chawla, N.V. Evaluating link prediction methods. Knowl Inf Syst 45, 751–782 (2015).` https://doi.org/10.1007/s10115-014-0789-0	2015
2	`Liben-nowell, David & Kleinberg, Jon. (2003). The Link Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology.`https://doi.org/58.10.1002/asi.20591	2003
2	`...`	...

Future work

Modulate the functions
Add more algorithms
Run on CUDA cores
Make it faster using vectorization etc.
Add more preprocessors
Add dataset, graph, and dataframe manipulations
Unify and reconstruct the architecture and eliminate redundancy

Notes

It can export the graph in .json and .gexf format for further usages. For instance: Gephi etc.

If you found it helpful, please give us a ⭐

License

Released under the BSD license

Copyright © 2017-2021 BiGraph Developers
Soran Ghadri (soran.gdr.cs@gmail.com)
Taleb Zarhesh (taleb.zarhesh@gmail.com)

hyphinslutionter/bigraph