基于快速局部移动迭代贪心算法的复杂网络社区发现
Complex network community discovery using fast local move iterated greedy algorithm
AI总结：
针对复杂网络社区发现问题，提出融合快速局部移动启发式与迭代贪心机制、结合随机邻居移动和改进Prune Louvain的FLMIG算法，在合成与真实网络上准确率和计算效率均优于现有前沿方法，为大规模动态网络分析提供支持。
摘要：
研究复杂网络中的社区结构对于理解其内在动力学和功能至关重要。本文提出了快速局部移动迭代贪心（Fast Local Move Iterated Greedy, FLMIG）算法，这是一种旨在有效识别复杂网络中社区结构的新方法。FLMIG算法通过引入快速局部移动启发式方法和在破坏阶段与构建阶段之间切换以强化社区划分的迭代贪心机制，改进了模块度优化过程。其主要创新点在于将随机邻居移动与改进的Prune Louvain算法相结合，在保证识别出的社区连通性的同时实现快速收敛。我们在合成网络和真实世界网络上开展的综合研究结果清楚表明，FLMIG在准确性和计算效率两方面均优于现有前沿技术。该算法不仅为社区识别提供了有力工具，还通过提供一种可有效处理大规模且动态演化网络的方法，为更广泛的网络分析领域做出了宝贵贡献。
英文摘要：
Examining the community structures within intricate networks is crucial for comprehending their intrinsic dynamics and functionality. The paper presents the Fast Local Move Iterated Greedy (FLMIG) algorithm, a novel method designed to effectively identify community structures in intricate networks. The FLMIG algorithm improves the modularity optimization process by including a rapid local move heuristic and an iterated greedy mechanism that switches between destructive and constructive phases to strengthen the community partitions. The main innovation is the integration of random neighbor moves with an enhanced Prune Louvain algorithm, which guarantees fast convergence while maintaining the connection of the identified communities. The results of our comprehensive studies, conducted on both synthetic and and real-world networks, clearly show that FLMIG surpasses existing cutting-edge techniques in terms of both accuracy and computing efficiency. This algorithm not only provides a strong tool for identifying communities, but also makes a valuable contribution to the broader field of network analysis by offering a method that can effectively handle large-scale and dynamically evolving networks.
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