{"id":848305,"date":"2022-05-26T21:22:41","date_gmt":"2022-05-27T04:22:41","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/"},"modified":"2022-06-16T18:55:01","modified_gmt":"2022-06-17T01:55:01","slug":"reinforcement-subgraph-reasoning-for-fake-news-detection","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/reinforcement-subgraph-reasoning-for-fake-news-detection\/","title":{"rendered":"Reinforcement Subgraph Reasoning for Fake News Detection"},"content":{"rendered":"
\n
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.<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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 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