We propose a memetic approach to find bottlenecks in complex networks based on searching for a graph partitioning with minimum conductance. Finding the optimum of this problem, also known in statistical mechanics as the Cheeger constant, is one of the most interesting NP-hard network optimisation problems. The existence of low conductance minima indicates bottlenecks in complex networks. However, the problem has not yet been explored in depth in the context of applied discrete optimisation and evolutionary approaches to solve it. In this paper, the use of a memetic framework is explored to solve the minimum conductance problem. The approach combines a hybrid method of initial population generation based on bridge identification and local optima sampling with a steady-state evolutionary process with two local search subroutines. These two local search subroutines have complementary qualities. Efficiency of three crossover operators is explored, namely one-point crossover, uniform crossover, and our own partition crossover. Experimental results are presented for both artificial and real-world complex networks. Results for Barabási–Albert model of scale-free networks are presented, as well as results for samples of social networks and protein–protein interaction networks. These indicate that both well-informed initial population generation and the use of a crossover seem beneficial in solving the problem in large-scale.

我们提出了一种模因方法，该方法基于搜索具有最小电导的图分区来发现复杂网络中的瓶颈。寻找此问题的最佳值，在统计力学中也称为Cheeger常数，是最有趣的NP硬网络优化问题之一。低电导最小值的存在指示复杂网络中的瓶颈。但是，在应用离散优化和解决该问题的进化方法的背景下，尚未对该问题进行深入探讨。在本文中，探索了使用模因框架来解决最小电导问题。该方法结合了基于桥梁识别和局部最优采样的初始种群生成的混合方法，以及具有两个局部搜索子例程的稳态进化过程。这两个本地搜索子例程具有互补的性质。探索了三个交叉算子的效率，即单点交叉，均匀交叉和我们自己的分区交叉。给出了针对人工和现实世界的复杂网络的实验结果。介绍了无标度网络的Barabási-Albert模型的结果，以及社交网络和蛋白质-蛋白质相互作用网络的样本结果。这些表明，消息灵通的初始种群生成和交叉使用似乎都有利于大规模解决该问题。给出了针对人工和现实世界的复杂网络的实验结果。介绍了无标度网络的Barabási-Albert模型的结果，以及社交网络和蛋白质-蛋白质相互作用网络的样本结果。这些表明，消息灵通的初始种群生成和交叉使用似乎都有利于大规模解决该问题。给出了针对人工和现实世界的复杂网络的实验结果。介绍了无标度网络的Barabási-Albert模型的结果，以及社交网络和蛋白质-蛋白质相互作用网络的样本结果。这些表明，消息灵通的初始种群生成和交叉使用似乎都有利于大规模解决该问题。