Reinforcement Subgraph Reasoning for Fake News Detection

Jun 1, 2022·
Ruichao Yang
,
Xiting Wang
Yiqiao Jin
Yiqiao Jin
,
Chaozhuo Li
,
Jianxun Lian
,
Xing Xie
· 1 min read
Figure showing the main model architecture and workflow
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. We introduce several novel technical components. First, 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. Second, we design a curriculum-based optimization method to ensure better convergence and train the two parts in an end-to-end manner. Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed framework.
Type
Publication
KDD 2022, Oral Presentation

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.

We introduce several novel technical components. First, 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. Second, we design a curriculum-based optimization method to ensure better convergence and train the two parts in an end-to-end manner.

Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed framework.

Keywords

Fake News Detection, Graph Reasoning, Subgraph Analysis, Reinforcement Learning, Graph Neural Networks, Explainable AI

Fake News Detection Graph Reasoning Subgraph Analysis Reinforcement Learning Graph Neural Networks Explainable AI
Yiqiao Jin
Authors
Yiqiao Jin
Final-year Ph.D. Candidate in Computer Science
My research sits at the intersection of multimodal foundation models, intelligent agents, and spatial/physical intelligence. I build general-purpose models that perceive, reason, learn, and act in interactive virtual and physical environments.