  Finding communities or clusters in social networks is a fa- mous topic in
social network analysis. Most algorithms are limited to static snapshots, so
they cannot handle dynamics within the underlying graph. In this paper we
present a modification of the Louvain community detec- tion method to handle
changes in the graph without rerunning the full algorithm. Also, we adapted the
Louvain greedy approach to optimize the Infomap measure. The main idea is, to
recalculate only a small area around the changes. Depending on the graph size
and the amount of changes, this yields a massive runtime decrease. As
validation data, we provide a graph generator, which produces spe- cific
community structures, at given times and also intermediate steps to transform
the graph from one to another specific graph. Experiments show that runtime
decrease is possible without much loss of quality. These values depend on the
reprocessed area inside the graph.
