  Community detection is of great importance for online social network
analysis. The volume, variety and velocity of data generated by today's online
social networks are advancing the way researchers analyze those networks. For
instance, real-world networks, such as Facebook, LinkedIn and Twitter, are
inherently growing rapidly and expanding aggressively over time. However, most
of the studies so far have been focusing on detecting communities on the static
networks. It is computationally expensive to directly employ a well-studied
static algorithm repeatedly on the network snapshots of the dynamic networks.
We propose DynaMo, a novel modularity-based dynamic community detection
algorithm, aiming to detect communities of dynamic networks as effective as
repeatedly applying static algorithms but in a more efficient way. DynaMo is an
adaptive and incremental algorithm, which is designed for incrementally
maximizing the modularity gain while updating the community structure of
dynamic networks. In the experimental evaluation, a comprehensive comparison
has been made among DynaMo, Louvain (static) and 5 other dynamic algorithms.
Extensive experiments have been conducted on 6 real-world networks and 10,000
synthetic networks. Our results show that DynaMo outperforms all the other 5
dynamic algorithms in terms of the effectiveness, and is 2 to 5 times (by
average) faster than Louvain algorithm.
