Social network analysis has become an important topic for researchers in sociology and computer science. Similarities among individuals form communities as the basic constitutions of social networks. Regarding the importance of communities, community detection is a fundamental step in the study of social networks typically modeled as large-scale graphs. Detecting communities in such large-scale graphs which generally suffers from the curse of dimensionality is the main objective followed in this study. An efficient modularity-based community detection algorithm called MDPCluster is introduced in order to detect communities in large-scale graphs in a timely manner. To address the high dimensionality problem, first, a Louvain-based algorithm is utilized by MDPCluster to distinguish initial communities as super-nodes and then a Modified Discrete Particle Swarm Optimization algorithm, called MDPSO is leveraged to detect communities through maximizing modularity measure. MDPSO discretizes Particle Swarm Optimization using the idea of transmission tendency and also escapes from premature convergence thereby a mutation operator inspired by Genetic Algorithm. To evaluate the proposed method, six standard datasets, i.e., American College Football, Books about US Politics, Amazon Product Co-purchasing, DBLP, GR-QC and HEP-TH have been employed. The first two are known as synthetic datasets whereas the rest are real-world datasets. In comparison to eight state-of-the-art algorithms, i.e., Stationary Genetic Algorithm, Generational Genetic Algorithm, Simulated Annealing-Stationary Genetic Algorithm, Simulated Annealing-Generational Genetic Algorithm, Grivan–Newman, Danon and Label Propagation Algorithm, the results indicate the superiorities of MDCluster in terms of modularity, Normalized Mutual Information and execution time as well.

中文翻译：

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MDPCluster：大规模图中基于群的社区检测算法

社会网络分析已成为社会学和计算机科学研究人员的重要课题。个体之间的相似性形成社区作为社会网络的基本构成。关于社区的重要性，社区检测是研究通常建模为大规模图的社交网络的基本步骤。在这种通常遭受维数灾难的大规模图中检测社区是本研究的主要目标。为了及时检测大规模图中的社区，引入了一种称为 MDPCluster 的有效的基于模块化的社区检测算法。为了解决高维问题，首先，MDPCluster 利用基于 Louvain 的算法将初始社区区分为超级节点，然后利用称为 MDPSO 的改进离散粒子群优化算法通过最大化模块化度量来检测社区。MDPSO 使用传输趋势的思想对粒子群优化进行离散化，并且也避免了早熟收敛，从而是受遗传算法启发的变异算子。为了评估所提出的方法，使用了六个标准数据集，即美国大学橄榄球、美国政治书籍、亚马逊产品联合采购、DBLP、GR-QC 和 HEP-TH。前两个被称为合成数据集，而其余的则是真实世界的数据集。与八种最先进的算法相比，即平稳遗传算法，世代遗传算法，