  Many real-world graphs evolve with time. Identifying communities or clusters
on such graphs is an important problem. In this technical report, we extend
three dynamic approaches, namely, Naive-dynamic (ND), Delta-screening (DS), and
Dynamic Frontier (DF), to our multicore implementation of the Leiden algorithm,
an algorithm known for its high-quality community detection. Our experiments on
a server with a 64-core AMD EPYC-7742 processor demonstrate that ND, DS, and DF
Leiden achieve speedups of 1.25x, 1.24x, and 1.37x on large graphs with random
batch updates, compared to Static, ND, and DS Leiden, respectively. However, on
real-world dynamic graphs, ND Leiden performs the best, being on average 1.14x
faster than Static Leiden. We hope our early results serve as a starting point
for dynamic approaches to the Leiden algorithm on evolving graphs.
