MCMC Louvain for Online Community Detection
This work addresses the need for efficient online community detection in dynamic networks, representing an incremental improvement over existing methods.
The authors tackled the problem of dynamically maintaining community structures in large, evolving networks by introducing an algorithm that maximizes modularity using a randomized hierarchical clustering based on MCMC, effectively dynamizing the Louvain algorithm.
We introduce a novel algorithm of community detection that maintains dynamically a community structure of a large network that evolves with time. The algorithm maximizes the modularity index thanks to the construction of a randomized hierarchical clustering based on a Monte Carlo Markov Chain (MCMC) method. Interestingly, it could be seen as a dynamization of Louvain algorithm (see Blondel et Al, 2008) where the aggregation step is replaced by the hierarchical instrumental probability.