This project focuses on modeling and mitigating the spread of misinformation in dynamic social network graphs using a Deep Q-Network (DQN) agent. By using reinforcement learning, the project aims to optimize intervention policies and provide actionable insights into misinformation propagation and control.
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Objective: To develop a reinforcement learning-based system capable of simulating and mitigating the spread of misinformation on large-scale social networks.
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Key Features:
- Dynamic graph modeling of social networks.
- Policy optimization for misinformation intervention.
- Evaluation of intervention strategies.
- Insights into ethical considerations for policy-making.
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Core Techniques:
- Reinforcement Learning: Deep Q-Network (DQN) for sequential decision-making.
- Graph-based Modeling: Representation of users as nodes and interactions as edges using NetworkX.
- Data Visualization: Analysis and simulation using self-built network.
- Programming Language: Python 3.8+
- Core Libraries:
networkx: For graph modeling and analysis.numpy: For numerical computations.pandas: For data handling and preprocessing.matplotlib: For visualizing graph structures and results.torch: For implementing and training the DQN.