Community detection is the problem of recognizing natural divisions in networks. A relevant challenge in this problem is to find communities on rapidly evolving graphs. In this report we present our Parallel Dynamic Frontier (DF) Louvain algorithm, which given a batch update of edge deletions and insertions, incrementally identifies and processes an approximate set of affected vertices in the graph with minimal overhead, while using a novel approach of incrementally updating weighted-degrees of vertices and total edge weights of communities. We also present our parallel implementations of Naive-dynamic (ND) and Delta-screening (DS) Louvain. On a server with a 64-core AMD EPYC-7742 processor, our experiments show that DF Louvain obtains speedups of 179x, 7.2x, and 5.3x on real-world dynamic graphs, compared to Static, ND, and DS Louvain, respectively, and is 183x, 13.8x, and 8.7x faster, respectively, on large graphs with random batch updates. Moreover, DF Louvain improves its performance by 1.6x for every doubling of threads.

翻译：社区检测是识别网络中自然划分的问题。其中一个关键挑战是在快速演化的图中发现社区。本报告提出并行动态前沿（DF）Louvain算法，该算法针对边删除和插入的批量更新，能以最小开销增量式识别并处理图中近似的受影响顶点集，同时采用创新方法增量更新顶点的加权度与社区的总边权重。我们还实现了朴素动态（ND）与增量筛选（DS）Louvain的并行版本。在配备64核AMD EPYC-7742处理器的服务器上，实验表明：在真实动态图上，DF Louvain相比静态、ND和DS Louvain分别获得179倍、7.2倍和5.3倍的加速比；在随机批量更新的大规模图上，其速度分别提高183倍、13.8倍和8.7倍。此外，线程数每翻倍，DF Louvain性能提升1.6倍。