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neurips2020-scg

The code repository for Discovering Conflicting Groups in Signed Networks (NeurIPS 2020)

https://github.com/rctzeng/neurips2020-scg

Science Score: 44.0%

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Keywords

neurips neurips2020 polarization signed-networks social-good social-network-analysis
Last synced: 6 months ago · JSON representation ·

Repository

The code repository for Discovering Conflicting Groups in Signed Networks (NeurIPS 2020)

Basic Info
  • Host: GitHub
  • Owner: rctzeng
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 16.1 MB
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  • Stars: 15
  • Watchers: 1
  • Forks: 2
  • Open Issues: 0
  • Releases: 0
Topics
neurips neurips2020 polarization signed-networks social-good social-network-analysis
Created over 5 years ago · Last pushed over 4 years ago
Metadata Files
Readme License Citation

README.md

SCG: Discovering Conflicting Groups in Signed Networks (NeurIPS2020)

"Discovering Conflicting Groups in Signed Networks", Ruo-Chun Tzeng, Bruno Ordozgoiti, and Aristides Gionis, In Proc. of NeurIPS 2020. * paper, video.

1. Dependency

1.1. Ours SCG Methods

  • Python 3.7
  • NumPy 1.17
  • SciPy 1.3

1.2. Baselines

  • KOCG(KDD'16): install matlab_engine via the link https://se.mathworks.com/help/matlab/matlab_external/install-the-matlab-engine-for-python.html.
  • BNC/SPONGE: pip install git+https://github.com/alan-turing-institute/SigNet.git.

2. To Reproduce Experimental Results

2.1. Run-Experiments

All the required commands are in the bash file run.sh, you just need to run it. * chmod 755 run.sh to give execution permission. * ./run.sh.

2.2. Inspect-Results

  • Go to result/ folder.
  • Run python plot.py -o figs_reproduce will print out summarized result and plot figures to the specified folder, ex: figs_reproduce/

3. Note

All methods except SCG-R and KOCG should be exactly identical to our reported figures on real-world datasets in the paper. * The reason why re-running the baseline KOCG(KDD'16) might have slightly different result is because of its random initialization (roulette wheel selection). However, it is certain that their method results in much lower polarity scores than our SCG methods. * For the baseline SPONGE(AISTATS'19), we try both unnormalized and symmetric normalized normalization scheme in their SigNet implementation and report the best of the two in both real-world dataseta and synthetic m-SSBM networks.

Owner

  • Name: Ruochun Tzeng
  • Login: rctzeng
  • Kind: user
  • Location: Sweden

KTH PhD in graph mining.

Citation (CITATION.cff) 

# YAML 1.2
---
abstract: "This is the repository for Discovering Conflicting Groups in Signed Networks (NeurIPS 2020)."
authors:
  -
    family-names: Tzeng
    given-names: "Ruo-Chun"
    orcid: "https://orcid.org/https://orcid.org/0000-0002-4222-274X"
cff-version: "1.1.0"
date-released: 2020-12-08
license: MIT
message: "If you use this software, please cite it using these metadata."
repository-code: "https://github.com/rctzeng/SCG-NeurIPS2020"
title: "SCG: Discovering Conflicting Groups in Signed Networks"
...

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