We present a network community-detection technique based on properties that emerge from a nature-inspired flocking system. Our algorithm comprises two alternating mechanisms: first, we control the particles alignment in higher dimensional space and, second, we present an iterative process of edge removal. These mechanisms together can potentially reduce accidental alignment among particles from different communities and, consequently, the model can generate robust community-detection results. In the proposed model, a random-direction unit vector is assigned to each vertex initially. A nonlinear dynamic law is established, so that neighboring vertices try to become aligned with each other. After some time, the system stops and edges that connect the least-aligned pairs of vertices are removed. Then, the evolution starts over without the removed edges, and after enough number of removal rounds, each community becomes a connected component. The proposed approach is evaluated using widely accepted benchmarks and real-world networks. Experimental results reveal that the method is robust and excels on a wide variety of networks. For large sparse networks, the edge-removal process runs in quasilinear time, which enables application in large-scale networks. Moreover, the distributed nature of the process eases the parallel implementation of the model.

我们提出了一种基于自然启发的植绒系统中出现的特性的网络社区检测技术。我们的算法包括两种交替机制：首先，我们控制高维空间中的粒子对齐，其次，我们提出了边缘去除的迭代过程。这些机制一起可以潜在地减少来自不同社区的粒子之间的意外对齐，因此，该模型可以产生强大的社区检测结果。在所提出的模型中，最初为每个顶点分配了一个随机方向的单位向量。建立了非线性动力学定律，以便相邻顶点尝试彼此对齐。一段时间后，系统停止并移除连接最少对齐顶点对的边。然后，进化在没有移除边缘的情况下重新开始，经过足够多的移除轮次后，每个社区都成为一个连接的组件。所提出的方法是使用广泛接受的基准和现实世界网络进行评估的。实验结果表明，该方法是稳健的，并且在各种网络上表现出色。对于大型稀疏网络，边缘去除过程在拟线性时间内运行，这使得在大规模网络中的应用成为可能。此外，该过程的分布式特性简化了模型的并行实现。对于大型稀疏网络，边缘去除过程在拟线性时间内运行，这使得在大规模网络中的应用成为可能。此外，该过程的分布式特性简化了模型的并行实现。对于大型稀疏网络，边缘去除过程在拟线性时间内运行，这使得在大规模网络中的应用成为可能。此外，该过程的分布式特性简化了模型的并行实现。