Awesome Open Source
Awesome Open Source


PyPI version PyPI version PyPI - Python Version

[comment]: <> (PyPI - Downloads)

[comment]: <> (PyPI - Downloads)

[comment]: <> (GitHub search hit counter)

[comment]: <> (GitHub search hit counter)

[comment]: <> (PyPI - Implementation)

[comment]: <> (GitHub commit activity)

[comment]: <> (GitHub last commit) GitHub Repo stars

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

  • Bug reports:

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


Install the latest version of BiGraph:

$ pip install bigraph

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

# Executing the function

if __name__ == '__main__':

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__':

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


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 that are calculated during evaluation:

Metrics table
Number Evaluattion metrics
1 Precision
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
    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__':


References table
Number Reference Year
1 Yang, Y., Lichtenwalter, R.N. & Chawla, N.V. Evaluating link prediction methods. Knowl Inf Syst 45, 751782 (2015). 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. 2003
2 ... ...

Future work

  • [x] 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


  • 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 ⭐️


Released under the BSD license

Related Awesome Lists
Top Programming Languages
Top Projects

Get A Weekly Email With Trending Projects For These Topics
No Spam. Unsubscribe easily at any time.
Python (795,156
Machine Learning (36,477
Deep Learning (35,955
Graph (24,408
Graph Algorithms (980
Graph Neural Networks (685
Deep Learning Algorithms (122
Link Prediction (100
Graph Analysis (57
Bipartite Graphs (21
Bigraph (5
Graph Link Prediction (3