@inproceedings{yang2022reinforcement,
author = {Yang, Ruichao and Wang, Xiting and Jin, Yiqiao and Li, Chaozhuo and Lian, Jianxun and Xie, Xing},
title = {Reinforcement Subgraph Reasoning for Fake News Detection},
booktitle = {ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD)},
year = {2022},
month = {June},
abstract = {The wide spread of fake news has caused serious societal issues. We propose a subgraph reasoning paradigm for fake news detection, which provides a crystal type of explainability by revealing which subgraphs of the news propagation network are the most important for news verification, and concurrently improves the generalization and discrimination power of graph-based detection models by removing task-irrelevant information. In particular, we propose a reinforced subgraph generation method, and perform fine-grained modeling on the generated subgraphs by developing a Hierarchical Path-aware Kernel Graph Attention Network. We also design a curriculum-based optimization method to ensure better convergence and train the two parts in an end-to-end manner. Extensive experiments show that our model outperforms the state-of-the-art methods and demonstrate the explainability of our method.},
url = {http://approjects.co.za/?big=en-us/research/publication/reinforcement-subgraph-reasoning-for-fake-news-detection/},
}