DRL-Rumor-Mitigation

Overall Framework

Installation

Environment

• **Tested OS: **Linux
• Python >= 3.8
• PyTorch == 1.13.0
• Tensorboard

Dependencies:

1. Install PyTorch 1.13.0 with the correct CUDA version.
2. Set the following environment variable to avoid problems with multiprocess trajectory sampling:

   export OMP_NUM_THREADS=1
   

Training

You can train your own models using the provided config in metro/cfg:

python -m news.train --cfg cfg_name --global_seed 0 --num_threads 1 --gpu_index 2 --agent rl-gnn3 

You can replace cfg_name to train other cfgs.

The results are saved in path result/platform/method/cfg/seed

Algorithm Framework

Result

• We compared DRLE to other baselines at different social media platforms, with the metrics being the Total Infectious Rate.
• Extensive experiments demonstrate that DRLE yields impressive effects on the mitigation of rumors, exhibiting an improvement of over 20% compared to baseline methods.

Transferability

• The model trained on small social media platforms can be directly applied to larger networks with only a marginal decrease in metrics. Importantly, this performance remains superior to the optimal baselines.

For Vulnerable Populations

• DRLE can also offer effective protection for specific populations within social media platforms.

License

Please see the license for further details.

Note

The implemention is based on Transform2Act.