Reinforcement Subgraph Reasoning for Fake News Detection

Image credit: Yiqiao Jin

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 \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.

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

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Yiqiao Jin
Yiqiao Jin
Research Intern at Microsoft Research Asia (MSRA)

My research interests include distributed robotics, mobile computing and programmable matter.