https://github.com/adirthaborgohain/community-data-analysis

Data and Visual Analysis on several different communities generated using Louvain Algorithm in Neo4j on the dblp dataset.
https://github.com/adirthaborgohain/community-data-analysis

data-analysis lda python

Last synced: about 14 hours ago
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Data and Visual Analysis on several different communities generated using Louvain Algorithm in Neo4j on the dblp dataset.

• Host: GitHub
• URL: https://github.com/adirthaborgohain/community-data-analysis
• Owner: AdirthaBorgohain
• License: mit
• Created: 2021-08-16T04:50:49.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-08-21T06:56:49.000Z (over 3 years ago)
• Last Synced: 2024-12-11T14:47:25.966Z (about 2 months ago)
• Topics: data-analysis, lda, python
• Language: Jupyter Notebook
• Homepage:
• Size: 56.1 MB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Community-Data-Analysis

Data and Visual Analysis on several different communities generated using Louvain Algorithm in Neo4j on the dblp dataset.

### Steps to run:

1. Install libraries and modules listed in requirements.txt. `pip install -r requirements.txt`

2. Download fasttext magnitude vectors from http://magnitude.plasticity.ai/fasttext/medium/wiki-news-300d-1M-subword.magnitude . Create a directory named `vectors` and place the downloaded magnitude file inside it.

3. To generate the complete JSON file for analysis, run the `generate_complete_JSON.py` file once.

4. All the individual notebooks with community id can be run independently for analysis of that specific community. The`Top10CommunitiesAnalysis.ipynb` file can be run for performing analysis as a whole for all the communities.