Python 3.8.3 and Pytorch 1.12.1 implementation of the latent Signed relational Latent dIstance Model (SLIM).
Graph representation learning has become a prominent tool for the characterization and understanding of the structure of networks in general and social networks in particular. Typically, these representation learning approaches embed the networks into a low-dimensional space in which the role of each individual can be characterized in terms of their latent position. A major current concern in social networks is the emergence of polarization and filter bubbles promoting a mindset of ''us-versus-them'' that may be defined by extreme positions believed to ultimately lead to political violence and the erosion of democracy. Such polarized networks are typically characterized in terms of signed links reflecting likes and dislikes. We propose the latent Signed relational Latent dIstance Model (SLIM) utilizing for the first time the Skellam distribution as a likelihood function for signed networks and extend the modeling to the characterization of distinct extreme positions by constraining the embedding space to polytopes.
conda create -n ${env_name} python=3.8.3
conda activate ${env_name}
pip install -r requirements.txt
Our Pytorch implementation uses the pytorch_sparse package. Installation guidelines can be found at the corresponding Github repository.
pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-1.12.1+cpu.html
pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-1.12.1+${CUDA}.html
where ${CUDA} should be replaced by either cu102, cu113, or cu116 depending on your PyTorch installation.
RUN: python main.py
optional arguments:
--epochs number of epochs for training (default: 5K)
--pretrained uses pretrained embeddings for link prediction (default: True)
--cuda CUDA training (default: True)
--LP performs link prediction (default: True)
--D dimensionality of the embeddings (default: 8)
--lr learning rate for the ADAM optimizer (default: 0.05)
--dataset dataset to apply Skellam Latent Distance Modeling on (default: wiki_elec)
--sample_percentage sample size network percentage, it should be equal or less than 1 (default: 0.3)
--reg_strength Regularization strength over the model parameters (default: 0.5 equivalent to normal priors) (ONLY FOR DIRECTED NETWORKS)
RUN: python main.py --cuda False --D 3 --pretrained False
RUN: python main_dir.py
The code has been primarily constructed and optimized for running in a GPU-enabled environment, running the code in CPU is significantly slower.
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model, Nikolaos Nakis et al., AISTATS 23
