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DRL-Rumor-Mitigation

Overall Framework

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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

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Result

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  • 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

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  • 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

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  • 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.