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Python package for link prediction in bipartite graphs and networks

Project description

Bipartite link prediction

Predicting links in bipartite networks on top of networkx

Node based similarities and Katz has been implemented. you can find algorithms in prediction directory. Algorithms untill now:

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

How to use the code

you can run the code by placing your data in inputs folder and use predict methodes iplemented in "predict" file or directly call provided functions in the evaluation file.

from bigraph import bigraph as bg

def main():
  """
  Link prediction on a bipartite network
  :return: Predicted linkes
  """
  df, df_nodes = import_files()
  G = make_graph(df)
  pr.aa_predict(G)  # Here we have called Adamic Adar method from bigraph module

or you can run evaluation methods directly which calls its peer method automatically

from bigraph.evaluation import evaluation as ev


def main():
    """
    Link prediction on a bipartite network
    :return: Predicted linkes
    """
    df, df_nodes = import_files()
    G = make_graph(df)
    ev.evaluate(G, k=10,
                method='all')  # Here we have evaluated all methods using evaluation module. Methods are 'jc', 'aa', 'pa', 'cn'

Metrics

Metrics that are calculated during evaluation:

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:

Row Left side element Right side element Weight*
1. ll0 rl1 1
2. ll2 rl1 1
3. ll1 rl2 1
4. ll3 rl3 1
5. ll4 rl3 2
  • Although the weight has not been involved in current version, but, the format will be the same.

More examples

from bigraph import bigraph as bg


def main():
  """
  Link prediction on a bipartite network
  :return: Predicted linkes
  """
  df, df_nodes = import_files()
  G = make_graph(df)
  pr.aa_predict(G)  # Here we have called Adamic Adar method from bigraph module
  pr.pa_predict(G)  # Prefferencial attachment
  pr.jc_predict(G)  # Jaccard coefficient
  pr.cn_predict(G)  # Common neighbors

References

Number Reference
1. Yang, Y., Lichtenwalter, R.N. & Chawla, N.V. Knowl Inf Syst (2015) 45: 751. https://doi.org/10.1007/s10115-014-0789-0
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
3. ...

TODO

  • <input type="checkbox" checked="" disabled="" /> Modulate the functions
  • <input type="checkbox" disabled="" /> Make it faster using vectorization etc.
  • <input type="checkbox" disabled="" /> Unify and reconstruct the architecture and eliminate redundancy

Documentation

I will provide documentations whenever I could make time!:watch: or you can pull a request and help to make it happen together

  1. After running the main, it will export the graph in .json and .gexf format for furthur usages. For instance: Gephi etc.

If it was helpful then hit the :star:

Project details


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