SIASM

Paper

"Adversarial Socialbots Modeling Based on Structural Information Principles."

Specification of dependencies

• Python version 3.8
• Check req.txt file for details. Basically, we will need torch, ray[rllib], tensorflow, networkx, and other basic packages. All other libraries and their version are stored in req.txt file. Or you can install all of the libraries by running:

conda create --name <env> --file req.txt

• Install the gym_bot environment:

cd gym_bot
python -m pip install -e .

• The main gym environment file is at ./gym-bot/gym_bot/envs/advbot_env_single_detect_large_hiar.py.

Dataset

• All the collected 100 news propagation networks are stored in ./database/_hoaxy#.pkl with # ranges from 0-99. These are networkx python object for convenient loading with networkx library.
• The test script (below) will automatically load the train and test portion of the dataset.

BlackBox Bot Detector

• We also provide the blackbox bot detector trained on the configurations specified in the paper at ./detector/RandomForestClassifier_TRAM_lengthNone.joblib. This is a scikit-learn Random Forest Classifier object that can be loaded using pickle library.
• Please refer to ./gym-bot/gym_bot/envs/advbot_env_single_detect_large_hiar.py to how to load and use this bot detector.

Hyper-Parameters and Model's Settings

Check the ppo_single_large_hiar.py.

config = {
    "NAME":'advbot-v6',
    "run_name":None, 
    "seed":SEED, 
    "probs":0.8, #set -1 to random
    "graph_algorithm":"node2vec", 
    "WALK_P":1, # parameter p of node2vec
    "WALK_Q":50, # parameter q of node2vec
    "model_type":"CONV", 
    "node_embed_dim":42, # node embedding dimension of node2vec
    "num_filters":8, # number of filters for CONV
    "validation_graphs":[],
    "reward_shaping":None, 
    "num_workers":5, # number of workers used during train/test
    "num_gpus":1, # number of GPUS
    "graph_feature":"gcn", # gcn means node2vec features
    "lr":0.0003, # learning rate
    "entropy_coeff":0.01, # ppo parameter
    "training_iteration":10000, # number of training iterations
    "checkpoint_freq":5, # frequency of saving checkpoints during training
    "wandb_key":"" #wandb API (replace with your own),
    "sip": True
}

config_test = {
    "custom_max_step": 120, # we train on 60 timesteps be default but during test we test on longer 120
    "detection_interval":20, # interval K refered in the paper
    "greedy": False, # whether test the AgentI+H in the paper (heuristic method)
}

Train

RUN: python ppo_single_large_hiar.py train

Test from Pre-trained Model

• To reproduce the reuslts, run: python ppo_single_large_hiar.py test ./checkpoint_best/checkpoint for SIASM and python ppo_single_large_hiar.py greedy ./checkpoint_best/checkpoint for SIASM-H baseline.