Matteo Serafino

Hi @neon-ninja when you say: > It depends what you mean by real-time. You can run it regularly. For example, you could run it every minute to check for new...

Is this bug fixed?

Hello @arnaudon , Thanks a lot for your answer. I see the problem with matrix exponentials. I could try the simplest version of the model. Basically you are suggesting to...

I understand. I can set the constructor to linearized. Also, setting the method to leiden speed up the process incredibly. You use the genealied Modularity. So the only point is...

Hello @d-schindler, Thanks for your asnwear. That would be great.

Hello @arnaudon, I did test the code as follow: `all_results = run(adjacency, min_scale=-1, max_scale = 1, n_scale=30, method='leiden', n_workers=6, constructor='linearized_directed')` As graphs, I generated some modular directed graph with `N`...

@d-schindler , I got the latest version, where you also fixed the following: `out_degrees = self.graph.toarray().sum(axis=1).flatten()` Which was causing a memory error. It seems is working fine. I will let...

Hello @d-schindler, The simulation went trough without problems. However, given the following parameters: ``` Is directed True Is weihed True N° nodes 179682 n scale: 30 ,max_scale: 5 ,min_scale: -1...

Hello @d-schindler, Thanks a lot. You mention that alpha=1 forces the algorithm on the strong connected component. Therefore, do you think would work properly in a graph that is weakly...

@d-schindler, Yes, I think it does. I would specify in the new documentation that for large networks, you use `\alpha=1` and also properly specify that this correspond on focusing on...