MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media

Installation

The codebase has been tested on Ubuntu 20.04.2 LTS using Python 3.8. To set up the environment and install the necessary dependencies, please follow the steps outlined below.

conda env create -f environment.yml

Implementation Details

MGM can be equipped with various GNN models:

GCN
GraphSAGE
GAT
SGC
DNA
FILM
FAGCN
GATv2Conv

Data

Data files are available in the data directory with the name of fact and bias. dataset.py is used to load the bias data. dataset_fact.py is used to load the data for factuality task.

MGM Training

To replicate our experimental results for the Factuality dataset, please use the following command to train the MGM based models:

python nmp_fact.py --cuda_id 0 --model [gcn/graphsage/gat/sgc/dna/gcnii/film/ssgc/fagcn/gatv2] --criterion sigmoid --hidden_dim [16,32,64..] --log_dir ./your_log --k 3 --eta [0.5,0.6,0.7,0.8,0.9,1] --val_test_batch_size 2 --epochs 50 --run_times 5 --normalize True --gnn_lr 0.001 --vae_lr 0.0001 --sim_function feature_base

To replicate our experimental results for the Bias dataset, please use the following command to train the MGM based models:

python nmp_bias.py --cuda_id 0 --model [gcn/graphsage/gat/sgc/dna/gcnii/film/ssgc/fagcn/gatv2] --criterion sigmoid --hidden_dim [16,32,64..] --log_dir ./your_log --k 3 --eta [0.5,0.6,0.7,0.8,0.9,1] --val_test_batch_size 2 --epochs 50 --run_times 5 --normalize True --gnn_lr 0.001 --vae_lr 0.0001 --sim_function feature_base

Descriptions for command arguments:

hidden_dim: The dimensionality of the hidden layer, matching the baseline's configuration as described in the main paper.

K: The number of globally similar nodes to consider.

eta: A hyperparameter controlling the balance between local and global information.