bigraph

Bipartite-network link prediction in Python

Science Score: 13.0%

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Keywords

bigraph bipartite-graphs bipartite-link-prediction deep-learning deep-learning-algorithms graph graph-algorithms graph-analysis graph-link-prediction graph-neural-network graph-neural-networks link-prediction machine-learning python

Last synced: 6 months ago · JSON representation

Repository

Bipartite-network link prediction in Python

Basic Info

Host: GitHub
Owner: bi-graph
License: other
Language: Python
Default Branch: master
Homepage: https://bigraph.readthedocs.io/en/latest/index.html
Size: 6.12 MB

Statistics

Stars: 84
Watchers: 2
Forks: 21
Open Issues: 6
Releases: 0

Topics

Created over 6 years ago · Last pushed almost 4 years ago

Metadata Files

Readme License

README.md

BiGraph

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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:

```python from bigraph.predict import aapredict from bigraph.preprocessing import importfiles, make_graph

def adamicadarprediction(): """ 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': adamicadarprediction() ```

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

```python from bigraph.evaluation.evaluation import evaluate from bigraph.preprocessing import importfiles, makegraph

def adamicadarevaluation(): """ Evaluate Adamic-Adar algorithm using 10-Fold cross-validation :return: A dictionary containing the evaluation results """ df, dfnodes = importfiles() 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': adamicadarevaluation() ```

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 importfiles
df, dfnodes = 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

```python from bigraph.predict import papredict, jcpredict, cnpredict from bigraph.preprocessing import importfiles, make_graph

def main(): """ Link prediction on bipartite networks :return: """ df, dfnodes = importfiles() G = makegraph(df) papredict(G) # Preferential attachment jcpredict(G) # Jaccard coefficient cnpredict(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

[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

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 :star:

License

Released under the BSD license

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

Owner

Name: BiGraph
Login: bi-graph
Kind: organization

Repositories: 2
Profile: https://github.com/bi-graph

Graph analysis libraries

GitHub Events

Total

Watch event: 4

Last Year

Watch event: 4

Committers

Last synced: over 2 years ago

All Time

Total Commits: 350
Total Committers: 4
Avg Commits per committer: 87.5
Development Distribution Score (DDS): 0.109

Past Year

Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Soran	s**s@g**m	312
Soran Qadri	k**d@g**m	29
sigma1326	t**h@g**m	8
The Codacy Badger	b**r@c**m	1

Committer Domains (Top 20 + Academic)

codacy.com: 1

Issues and Pull Requests

Last synced: over 2 years ago

All Time

Total issues: 7
Total pull requests: 6
Average time to close issues: about 3 hours
Average time to close pull requests: about 5 hours
Total issue authors: 3
Total pull request authors: 3
Average comments per issue: 0.29
Average comments per pull request: 0.5
Merged pull requests: 3
Bot issues: 0
Bot pull requests: 3

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

soran-ghaderi (5)
erlebach (1)
stonemasonSantiago (1)

Pull Request Authors

dependabot[bot] (3)
sigma1326 (2)
codacy-badger (1)

Top Labels

Issue Labels

Pull Request Labels

dependencies (3)

Packages

Total packages: 1
Total downloads:
- pypi 41 last-month

Total dependent packages: 0
Total dependent repositories: 0
Total versions: 3
Total maintainers: 2

pypi.org: bigraph

Python package for link prediction in bipartite graphs and networks

Homepage: https://github.com/bi-graph/bigraph
Documentation: https://bigraph.readthedocs.io/
License: BSD License
Latest release: 0.1rc7
published about 5 years ago

Versions: 3
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 41 Last month

Rankings

Stargazers count: 7.6%

Forks count: 8.4%

Dependent packages count: 10.0%

Average: 24.1%

Downloads: 26.8%

Dependent repos count: 67.6%

Maintainers (2)

soran taleb.zarhesh

Last synced: 6 months ago

Dependencies

pyproject.toml pypi

Pygments ^2.12.0
matplotlib ^3.5.2
networkx ^2.6.3
numpy ^1.22.4
pandas ^1.4.1
python >=3.10,<3.11
scikit-learn ^1.1.1
scipy ^1.8.1
tabulate ^0.8.9

requirements/default.txt pypi

matplotlib >=3.2.0
networkx >=2.4
numpy >=1.17.4
pandas >=0.25.2
scikit-learn >=0.24.2
setuptools *
tabulate >=0.8.7

requirements/developer.txt pypi

setuptools * development

requirements.txt pypi

bluepy *