https://github.com/abdcelikkanat/TNE

TNE: A Latent Model for Representation Learning on Networks
https://github.com/abdcelikkanat/TNE

Last synced: about 1 month ago
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TNE: A Latent Model for Representation Learning on Networks

• Host: GitHub
• URL: https://github.com/abdcelikkanat/TNE
• Owner: abdcelikkanat
• Created: 2018-05-28T15:33:30.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2023-07-06T21:31:45.000Z (over 1 year ago)
• Last Synced: 2024-08-01T17:36:01.692Z (4 months ago)
• Language: C++
• Homepage: https://abdcelikkanat.github.io/projects/TNE/
• Size: 14.6 MB
• Stars: 5
• Watchers: 2
• Forks: 1
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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• awesome-community-detection - [Python Reference

README

        
# TNE

Topic-Aware Latent Models for Representation Learning on Networks

#### Installation

##### Anaconda installation

1.  Clone the repository by typing the following command:

```

git clone https://github.com/abdcelikkanat/TNE.git

```

2. To initialize a new environment for Python 3.6 and to activate it, run the commands

```

conda create -n tne python=3.6

source activate tne

```

3. Install all the required modules.

```

pip install -r requirements.txt

```

**Note:** _It may be required to compile the C extension of *gensim* package for a faster training process so you can run the following command inside the *"ext/gensim_wrapper/models/"* folder:_

```

python setup.py install

```

_and you should copy the output **.so** file into the same directory._

#### How to run

An example to learn node representations with *Louvain* community detection method might be

```

python run.py --corpus ./examples/corpus/karate.corpus --graph_path ./examples/datasets/karate.gml --emb ./karate.embedding --comm_method louvain

```

Similarly, we can adopt *LDA* algorithm in learning node representations.

```

python run.py --corpus ./examples/corpus/karate.corpus --emb ./karate.embedding --comm_method lda --K 2

```

You can view all the detailed list of commands by typing

```

python run.py -h

```

#### External Libraries

i) You might need to compile the source codes of **BigClam** and **GibbsLDA** algorithms for your operating system and place the executable files into suitable directories. You can also configure some parameters defined in the *consts.py* file.