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:

frombigraph.predictimportaa_predictfrombigraph.preprocessingimportimport_files, make_graphdefadamic_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 modulereturnpredicted# Executing the functionif__name__=='__main__':
adamic_adar_prediction()

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

frombigraph.evaluation.evaluationimportevaluatefrombigraph.preprocessingimportimport_files, make_graphdefadamic_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'returnresults# Executing the functionif__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

frombigraph.predictimportpa_predict, jc_predict, cn_predictfrombigraph.preprocessingimportimport_files, make_graphdefmain():
""" Link prediction on bipartite networks :return: """df, df_nodes=import_files()
G=make_graph(df)
pa_predict(G) # Preferential attachmentjc_predict(G) # Jaccard coefficientcn_predict(G) # Common neighbors# Executing the functionif__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)