Optimized network clustering by jumping sub-optimal dendrograms
We propose a method to improve community division techniques in networks that are based on agglomeration by introducing dendrogram jumping. The method is based on iterations of sub-optimal dendrograms instead of optimization of each agglomeration step. We find the algorithm to exhibit excellent scaling behavior of its computational complexity. In its present form the algorithm scales as $\mathcal{O} (N^{2})$, but by using more efficient data structures it is possible to achieve a scaling of $\mathcal{O} (N \log^{2} N)$. We compare our results with other methods such as the greedy algorithm and the extremal optimization method. We find modularity values larger than the greedy algorithm and values comparable to the extremal optimization method.