  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.
