Parameter tuning for modularity maximization

Develop methods to tune the parameters of modularity maximization algorithms to obtain the optimal network partition.

Background

Modularity maximization seeks an optimal partition of a network into communities by optimizing a global quality function, but the search landscape is rugged with many local maxima and known resolution limits, making the problem computationally hard.

The authors note that while heuristic approaches (e.g., simulated annealing with temperature sweeps) are commonly used, there is no clear guidance on how to tune algorithmic parameters to reliably achieve the optimal solution.