Dynamic Graph Partitioning for Large-Scale Data Analysis

Abstract

Large-scale graph analysis, prevalent in domains like social network analysis, bioinformatics, and knowledge graph management, faces significant challenges due to the memory limitations of traditional graph processing techniques. This paper introduces a dynamic graph partitioning algorithm designed to mitigate these issues. The core of the algorithm employs a reinforcement learning (RL) agent that intelligently adapts the graph's structure during the analysis process, responding to query patterns and data access patterns. The goal is to minimize communication overhead and maximize computational efficiency. Specifically, the algorithm learns to identify and isolate subgraphs relevant to ongoing analysis, effectively reducing the scope of computations and data transfers. This dynamic adaptation provides a crucial advantage over static partitioning approaches, enabling more efficient and scalable graph analysis on datasets that would otherwise be intractable. The proposed method leverages RL to optimize the partitioning strategy in real-time, leading to improved performance and reduced resource consumption. The effectiveness of the approach is demonstrated through the theoretical framework and algorithmic design, outlining a pathway for future research and practical implementation.