I am a new user of the library so forgive if my questions are very basic.

    I am using the minimize_nested_blockmodel_dl in order to create a hierarchical cluster for a graph.

    The graph is created from a matrix distance of the nodes in the graph. There are 180 nodes.

    I have tried with different transformations of the distances to weights, basically w=1-d and w=1/d, previously normalizing to fit in range 0-1.

    Only with the second one I have been able to obtain a non-trivial partition and the partition obtained is quite bad (low NMI and high NVI ... ).

    Using other methods, much simpler, I have found much better partition (agglomerative clustering for example) so I know the data allows for a better partition to be obtained.

    

    The method is non-parametric, once the weights are provided, except for the number of sweeps, epsilons,...

     How can I know if I need to increase the number of sweeps or if I have reached the limit of the algorithm?

     So far, I have run upto 10K sweeps but there is no much difference in the execution time nor the result.

      What other parameters can be adjusted and under what criteria?

      How must the data be normalized to obtain optimal results(increase algorithm chances of finding a better partition series)?

       Thanks in advance

      

          Jose