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This study investigates the application of Reinforcement Learning (RL) to network dissection, aiming to assess its potential for uncovering meaningful structures within complex networks. Unlike traditional approaches that rely heavily on predefined network characteristics, we introduce an RL agent guided by a reward function shaped with minimal expert input. The objective is to enable the agent to autonomously identify patterns and structures, such as influence domains or functional modules, across diverse network types. Using a combination of simulated and real-world datasets, our methodology evaluates the agent’s performance in terms of efficiency and accuracy compared to standard analytical techniques. Preliminary results demonstrate a \(25\%\) improvement in identifying key structures while reducing computational overhead by \(15\%\) . These findings highlight the potential of RL in augmenting network analysis, particularly in scenarios where domain expertise is limited. Future work will focus on scaling the approach to larger networks and integrating multi-agent systems for collaborative analysis.

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Partially Guided Reinforcement Learning Approach of Reward Adjustment for Dissecting Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Rami Mohammed Alroobi,
  • Mir Ali,
  • Connor White,
  • Kyle Ybanez

摘要

This study investigates the application of Reinforcement Learning (RL) to network dissection, aiming to assess its potential for uncovering meaningful structures within complex networks. Unlike traditional approaches that rely heavily on predefined network characteristics, we introduce an RL agent guided by a reward function shaped with minimal expert input. The objective is to enable the agent to autonomously identify patterns and structures, such as influence domains or functional modules, across diverse network types. Using a combination of simulated and real-world datasets, our methodology evaluates the agent’s performance in terms of efficiency and accuracy compared to standard analytical techniques. Preliminary results demonstrate a \(25\%\) improvement in identifying key structures while reducing computational overhead by \(15\%\) . These findings highlight the potential of RL in augmenting network analysis, particularly in scenarios where domain expertise is limited. Future work will focus on scaling the approach to larger networks and integrating multi-agent systems for collaborative analysis.