Reinforcement Subgraph Reasoning for Fake News Detection - Yang, Ruichao; Wang, Xiting; Jin, Yiqiao; Li, Chaozhuo; Lian, Jianxun; Xie, Xing (ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD), 2022)
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.