  Existing modularity-based community detection methods attempt to find
community memberships which can lead to the maximum of modularity in a fixed
graph topology. In this work, we propose to optimize the graph topology through
the modularity maximization process. We introduce a modularity-guided graph
optimization approach for learning sparse high modularity graph from
algorithmically generated clustering results by iterative pruning edges between
two distant clusters. To the best of our knowledge, this represents a first
attempt for using modularity to guide graph topology learning. Extensive
experiments conducted on various real-world data sets show that our method
outperforms the state-of-the-art graph construction methods by a large margin.
Our experiments show that with increasing modularity, the accuracy of
graph-based clustering algorithm is simultaneously increased, demonstrating the
validity of modularity theory through numerical experimental results of
real-world data sets. From clustering perspective, our method can also be seen
as a self-boosting clustering method.

Data Analysis, Statistics and Probability