An essential mesoscopic concept in network analysis is that of community structure. However, conventional nature-inspired optimization algorithms encounter serious challenges and difficulties when used directly to seek communities in networks, due to the large amount of data and the NP-hard combinatorial nature of the problem. Thus in this paper, we introduce a novel bacterial foraging optimization approach to uncovering community structure in networks. Instead of using the original bacterial foraging designed traditionally for continuous optimization, the problem of community detection is exquisitely embedded into a redefined discrete framework. The evolutionary principles for the bacterial foraging are developed from a topological perspective. Furthermore, two specific local updating rules, namely the greedy strategy and the stochastic strategy, are designed to steer the swarm of bacteria to the favored regions. The extensive experimental results on both synthetic and real-world networks indicate that the proposed approach outperforms the baseline algorithms and can achieve a high accuracy on the uncovered community structure. The integration of the proposed approach into the analysis of power grids and its explicit utility are also discussed in detail, showing that our method has high accuracy and practicability.

网络分析中一个基本的介观概念是社区结构。然而，由于数据量大和问题的 NP-hard 组合性质，传统的自然启发优化算法在直接用于在网络中寻找社区时遇到了严重的挑战和困难。因此，在本文中，我们引入了一种新的细菌觅食优化方法来揭示网络中的群落结构。不是使用传统上为持续优化设计的原始细菌觅食，而是将社区检测问题巧妙地嵌入到重新定义的离散框架中。细菌觅食的进化原理是从拓扑的角度发展起来的。此外，两个特定的局部更新规则，即贪婪策略和随机策略，旨在将细菌群引导到受青睐的区域。在合成网络和真实世界网络上的大量实验结果表明，所提出的方法优于基线算法，并且可以在未覆盖的社区结构上实现高精度。还详细讨论了将所提出的方法集成到电网分析及其显性效用中，表明我们的方法具有较高的准确性和实用性。