https://github.com/aryanahadinia/jwnmf

An Unofficial implementation of JWNMF: A Joint Weighted Nonnegative Matrix Factorization Model via Fusing Attribute Information for Link Prediction
https://github.com/aryanahadinia/jwnmf

social-network

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An Unofficial implementation of JWNMF: A Joint Weighted Nonnegative Matrix Factorization Model via Fusing Attribute Information for Link Prediction

• Host: GitHub
• URL: https://github.com/aryanahadinia/jwnmf
• Owner: AryanAhadinia
• Created: 2023-03-11T00:41:06.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-03-11T00:43:09.000Z (almost 2 years ago)
• Last Synced: 2023-09-12T04:11:02.232Z (over 1 year ago)
• Topics: social-network
• Language: Python
• Homepage: https://link.springer.com/chapter/10.1007/978-3-031-23902-1_15
• Size: 80.1 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# JWNMF

This is an **Unofficial** implementation of method proposed by Tang in [1].

## Sample Data

In this repository, we've placed a test data which is a sythensized graph generated using method 

proposed in [2] and simulated cascades of posts exchanged between social network nodes. Underlying

graph in this data has 100 nodes and we've simulated 200 i.i.d. cascades on that. To use this to

for testing proposed method in [1], we've randomly deleted some links and cascade participations.

## How to run

Convert data

```Python

python converter/convert.py -d 

```

Run Code

```Python

python main.py -d 

```

## Parameters

## References

[1] Tang, M. (2022). A Joint Weighted Nonnegative Matrix Factorization Model via Fusing Attribute 

Information for Link Prediction. In: Chenggang, Y., Honggang, W., Yun, L. (eds) Mobile Multimedia 

Communications. MobiMedia 2022. Lecture Notes of the Institute for Computer Sciences, Social 

Informatics and Telecommunications Engineering, vol 451. Springer, Cham. 

[2] Lancichinetti, A., Fortunato, S., & Radicchi, F. (2008). Benchmark graphs for testing 

community detection algorithms. Physical review E, 78(4), 046110.