Duch Jordi, Arenas Alex
Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain.
Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Aug;72(2 Pt 2):027104. doi: 10.1103/PhysRevE.72.027104. Epub 2005 Aug 24.
We propose a method to find the community structure in complex networks based on an extremal optimization of the value of modularity. The method outperforms the optimal modularity found by the existing algorithms in the literature giving a better understanding of the community structure. We present the results of the algorithm for computer-simulated and real networks and compare them with other approaches. The efficiency and accuracy of the method make it feasible to be used for the accurate identification of community structure in large complex networks.
我们提出了一种基于模块度值的极值优化来寻找复杂网络中社区结构的方法。该方法优于文献中现有算法所找到的最优模块度，能更好地理解社区结构。我们展示了该算法在计算机模拟网络和真实网络上的结果，并与其他方法进行比较。该方法的效率和准确性使其可用于准确识别大型复杂网络中的社区结构。
