Am 17.07.20 um 20:44 schrieb Tiago de Paula Peixoto:
> Am 17.07.20 um 20:35 schrieb Dominik Schlechtweg:> Thanks for clarifying
> this. Last question: Does your doubt also
>> concern the special case where alpha = 0, i.e., ignoring edge
>> probabilities completely? (This is the actually
Am 17.07.20 um 20:35 schrieb Dominik Schlechtweg:> Thanks for clarifying
this. Last question: Does your doubt also
> concern the special case where alpha = 0, i.e., ignoring edge
> probabilities completely? (This is the actually interesting case for
> us. We are not interested in tuning this
Am 17.07.20 um 19:44 schrieb Tiago de Paula Peixoto:
> Am 17.07.20 um 14:19 schrieb Dominik Schlechtweg:
>>> is there a way to suppress the likelihood of the edge probabilities as in
>>> [2] where the alpha-parameter can be used to fit "only to the weight
>>> information"? (Compare to formula
Am 17.07.20 um 14:19 schrieb Dominik Schlechtweg:
>> is there a way to suppress the likelihood of the edge probabilities as in
>> [2] where the alpha-parameter can be used to fit "only to the weight
>> information"? (Compare to formula (4) in [2].)
>> [...]
>> [2] C. Aicher, A. Z. Jacobs, and A.
thanks, short follow-up:
Am 17.07.20 um 12:36 schrieb Tiago de Paula Peixoto:
> Am 16.07.20 um 00:49 schrieb Dominik Schlechtweg:
>> Hi Tiago,
>>
>> we noticed that with certain weighted graphs minimize_blockmodel_dl() tends
>> to put hubs (vertices with many edges) into the same cluster. Please
Am 16.07.20 um 00:49 schrieb Dominik Schlechtweg:
> Hi Tiago,
>
> we noticed that with certain weighted graphs minimize_blockmodel_dl() tends
> to put hubs (vertices with many edges) into the same cluster. Please find a
> minimal example below, which produces the clustered graph in the attached
