HG-PD Model

This repository is the official implementation of the group polarization detection model proposed in our paper When "normal" becomes polarized: Heterogeneous graph clustering for non-ideological community conflicts.

Requirements

• Python version: 3.10.13

Usage Instruction

1. You can refer to Data_description.txt for more information about .csv .xlsx .pt and .npy files in our codes.
2. Code files Include 2 types:
   • Python scripts .py for collecting Sina Weibo's data (Sina_crawl) and some model-training related functions
   • Jupyter files .ipyn for (a) data processing; (b) all experiments in paper; and (c) visualization for HG-PD (i.e., exp3)
   • All Jupyter files are in 2 language versions, i.e., Chinese and English, for better understanding :D
3. Python scripts (.py)
   • Sina_crawl: Used for crawling the data we need from Sina weibo (You can use it for crawling other Sina Weibo posts)
   • userInter: HomoG-based model framework for exp2
   • mcr_HGPD: HG-PD model framework for exp3
   • mcrLoss: MCR2 loss function
   • augment: Data augmentation
   • other_func: Used for constructing membership matrix \Pi
   • savePara: Used for saving loss .csv and model states .pt
4. Jupyter files (.ipynb)
   • Data_processing: Include all data processing steps for 3 experiments
   • K-Prototype: Inmplementation of exp1 in our paper; Results are saved in Train_record/KPrototype
   • Ablation: Implementation of exp2 with related visualizations in our paper; Training results are saved in Train_record/Ablation and visualizations in Visualization
   • Model: Implementation of exp3 in our paper; Results are saved in Train_record/Model
   • Analysis_visualize: Visualizations of exp3 in our paper; Figures are saved in Visualization; Note that some additional visualizations are generated within Supplementary.ipynb and saved automatically to the specified output paths.
   • Abnormal_compar: Model Comparisons Experiments in our paper; including 5 GAD models for GP detection and corresponding analysis & visualization
   • Supplementary: Includes supplementary visualizations and result analyses, applicable to both the ablation experiments and the GAD models
5. About Train_record
   • We just put the best model state in Train_record folder
6. About Abnormal_result
   • Including (a) detection results (embedding, labels and scores) from all 5 GAD models; (b) csv file involved in analysis of all 5 GAD models; (c) tsne visualizations

Feel free to post any issues via Github.