The European Physical Journal B ( IF 1.6 ) Pub Date : 2021-07-07 , DOI: 10.1140/epjb/s10051-021-00122-x Xiaoyu Li 1 , Chao Gao 2 , Zhen Wang 2 , Xianghua Li 2 , Songxin Wang 3 , Chen Liu 4
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Abstract
The community structure, owing to its significant status, is of extraordinary significance in comprehending and detecting inherent functions in real networks. However, the community structures are always hard to be identified, and whether the existing algorithms are based on optimization or heuristics, the robustness and accuracy should be improved. The physarum (i.e., slime molds with multi heads) has proved its ability to produce foraging networks. Therefore, we adopt physarum so that the optimization-based community detection algorithms can work more efficiently. Specifically, a physarum-based network model (pnm), which is capable of identifying inter-edges of the community in a network, is used to optimize the prior knowledge of existing evolutional algorithms (i.e., genetic algorithm, particle swarm optimization algorithm and ant colony algorithm). the optimized algorithms have been compared with some advanced methods in synthetic and real networks. experimental results have verified the effectiveness of the proposed method.
Graphic abstract
中文翻译：
复杂网络中社区发现的一种新的自然启发优化
摘要
社区结构由于其重要的地位，在理解和检测真实网络中的固有功能方面具有非凡的意义。然而，社区结构总是难以识别，无论现有算法是基于优化还是启发式，都需要提高鲁棒性和准确性。的绒泡（即，粘菌多头）已经证明了它产生觅食网络的能力。因此，我们采用physarum使基于优化的社区检测算法可以更有效地工作。具体来说，酸浆基于网络模型（pnm），能够识别网络中社区的边缘，用于优化现有进化算法（即遗传算法、粒子群优化算法和蚁群算法）的先验知识。优化后的算法与合成网络和真实网络中的一些先进方法进行了比较。实验结果验证了所提出方法的有效性。
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