About this document
Deep Reinforcement Learning for Rumor Control by Angad Singh Ahuja is a document available to read on EtoBox.
This study presents a deep reinforcement learning-based approach to minimize rumor influence in social networks, addressing the dynamic nature of rumor propagation. It introduces the dynamic rumor influence minimization (DRIM) problem and a rumor-blocking strategy (RLDB) that adapts to changes in user states and network structure. Experimental results demonstrate the effectiveness of the proposed models over traditional static methods in controlling rumor spread across various real-world datasets.
- Author
- Angad Singh Ahuja
- Language
- EN