Dynamic Clustering in Social Networks Using Louvain and Infomap Method

Finding communities or clusters in social networks is a famous 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 Lou-vain community detection 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 specific community structures, at given times and also intermediate steps to transform the graph from one to another specific graph. In experiments we could decrease the runtime up to 35 %, with similar cluster results. These values depend on the reprocessed area inside the graph.