Reinforcement Subgraph Reasoning for Fake News Detection

Image credit: Yiqiao Jin


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 \xiting{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.

In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22)

Supplementary notes can be added here, including code, math, and images.

Yiqiao Jin
Yiqiao Jin
Graduate Research Assistant at Georgia Institute of Technology

My research interests include Computational Social Science, Misinformation, Graph Analysis, and Data Mining.