DynaMo: Dynamic Modularity-based Community Detection in Evolving Social Networks [article]

Di Zhuang, J. Morris Chang, Mingchen Li
2019 arXiv   pre-print
Community detection is one of the most popular research topics in the field of 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, nowadays, the real world networks (e.g., Facebook, LinkedIn and Twitter) are inherently evolving rapidly and expanding aggressively over time. However, most of the studies so far have been focusing on detecting communities on the
more » ... atic networks. It is computationally expensive to directly employ a well-studied static algorithm repeatedly on the network snapshots of the evolving networks. We propose DynaMo, a novel dynamic modularity-based community detection algorithm, aiming to detect communities in evolving social networks. DynaMo is an adaptive and incremental algorithm, which is designed for maximizing the modularity gain while updating the community structure of evolving networks. In the experimental evaluation, a comprehensive comparison has been made among our algorithm, Louvain algorithm (static) and 5 other dynamic algorithms. Extensive experiments have been conducted on 6 real world social networks. The experimental results show that DynaMo outperforms all the other 5 dynamic algorithms in terms of effectiveness, and achieves (on average) 2 to 5 times faster than Louvain algorithm.
arXiv:1709.08350v2 fatcat:l24anqejkfh7bfqliwgfljt6sm