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Abstract
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Community detection plays a critical role in various fields, including social network analysis, biology, and computer science, as it helps identify clusters or groups within large datasets. Traditional methods often struggle with dynamic graphs, where nodes and edges change over time. By integrating random walk techniques with evolutionary optimization, researchers can enhance the accuracy and efficiency of community detection algorithms, allowing for real-time analysis of rapidly changing networks. This approach addresses the limitations of static models and provides a more nuanced understanding of community dynamics in complex systems [1, 5, 6]. Random walk methods are particularly effective in exploring the relationships between nodes within a network. They simulate the movement of a hypothetical walker traversing the graph, which helps in identifying densely connected groups. The walk-likelihood algorithm (WLA), for example, utilizes random walks to partition nodes into communities based on their connectivity patterns. This method has demonstrated superior performance in terms of modularity compared to other community detection algorithms. When combined with evolutionary optimization techniques, which adaptively refine solutions over generations, the resulting framework can dynamically adjust to changes in the network structure while maintaining high detection accuracy[1, 7]. The integration of evolutionary strategies allows for improved scalability and robustness in community detection tasks. As networks grow larger and more complex, traditional algorithms may become computationally expensive or fail to capture overlapping communities. Evolutionary optimization can mitigate these issues by evolving solutions that adapt to new information while leveraging previous knowledge from earlier iterations. This synergy between random walks and swarm methods not only enhances the precision of community detection but also opens avenues for addressing ongoing challenges in
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