Community detection is a key aspect of network analysis, as it allows for the identification of groups and patterns within a network. With the ever-increasing size of networks, it is crucial to have fast algorithms to analyze them efficiently. It is a modularity-based greedy algorithm that divides a network into disconnected communities better over several iterations. Even in big, dense networks, it is renowned for establishing high-quality communities. However it can be at least a factor of ten slower than community discovery techniques that rely on label-propagation, which are generally extremely fast but obtain communities of lower quality. The researchers have suggested a number of methods for parallelizing and improving the Louvain algorithm. To decide which strategy is generally the best fit and which parameter values produce the highest performance without compromising community quality, it is critical to assess the performance and accuracy of these existing approaches. As we implement the single-threaded and multi-threaded versions of the static Louvain algorithm in this report, we carefully examine the method's specifics, make the required tweaks and optimizations, and determine the right parameter values. The tolerance between each pass can be changed to adjust the method's performance. With an initial tolerance of 0.01 and a tolerance decline factor of 10, an asynchronous version of the algorithm produced the best results. Generally speaking, according to our findings, the approach is not well suited for shared-memory parallelism; however, one potential workaround is to break the graph into manageable chunks that can be independently executed and then merged back together.

翻译：社区检测是网络分析的关键环节，能够识别网络中的群组与模式。随着网络规模持续增长，开发高效快速的分析算法至关重要。该算法基于模块度优化的贪心策略，通过多次迭代将网络划分为互不连通的社区结构。即便面对大规模密集网络，其构建优质社区的能力仍备受认可。然而，相较于依赖标签传播的社区发现技术（通常速度极快但社区质量较低），该算法可能慢至少一个数量级。研究者已提出多种并行化改进Louvain算法的方法。为确定最优通用策略及在不降低社区质量前提下实现最高性能的参数配置，评估现有方法的性能与精度至关重要。本报告在实现静态Louvain算法的单线程与多线程版本时，深入解析算法细节，进行必要调整与优化，并确定合适参数值。通过调整每轮迭代的容差参数可改变算法性能。实验表明，采用初始容差0.01、容差衰减因子为10的异步版本算法取得了最佳效果。总体而言，研究发现该算法不适用于共享内存并行架构；但可通过将图分割为可独立执行后合并的若干模块作为潜在解决方案。