Ni! Hi Alireza, Yep, that's what I meant.
As a note, this is what the adjacency matrix for any bipartite graph can be reordered to look like - or undirected bipartite graph in your particular case. Note that in SBM terms this corresponds to two blocks that only connect across, which is why one should impose that constraint using `pclabel`, sparing the algorithm from inferring it. (And in a hypergraph interpretation you likely wouldn't want blocks mixing hyperedges and nodes to start with.) []s ale .~´ On Tue, Mar 3, 2020 at 6:48 PM Alireza Kashani < [email protected]> wrote: > Dear Alexander, > > Thanks for your answer, regarding the treatment, > For example, if my data looks like > > > p1 p2 p3 p4 p5 p6 > j1 1 0 1 0 1 0 > j2 0 1 1 0 0 1 > j3 0 0 0 1 1 1 > j4 1 0 0 1 0 0 > > are you suggesting to treat it as > > > j1 j2 j3 j4 p1 p2 p3 p4 p5 p6 > j1 0 0 0 0 1 0 1 0 1 0 > j2 0 0 0 0 0 1 1 0 0 1 > j3 0 0 0 0 0 0 0 1 1 1 > j4 0 0 0 0 1 0 0 1 0 0 > p1 1 0 0 1 0 0 0 0 0 0 > p2 0 1 0 0 0 0 0 0 0 0 > p3 1 1 0 0 0 0 0 0 0 0 > p4 0 0 1 1 0 0 0 0 0 0 > p5 1 0 1 0 0 0 0 0 0 0 > p6 0 1 1 0 0 0 0 0 0 0 > > to be an adjacency matrix ? > Thanks, > > > On Tue, Mar 3, 2020 at 5:32 PM Alexandre Hannud Abdo <[email protected]> > wrote: > >> Ni! >> >> Incidence matrices can be trated as a subset of an adjacency matrix (and >> in this sense hypergraphs as a special case of graphs), so in principle >> there is no problem to work around. You just need to properly interpret the >> model in terms of membership to hyperedges instead of connections to nodes. >> >> You should hardcode the bipartite structure when running the DL >> minimization in order to save processing time (see the pclabel param). >> >> If you're convinced that this doesn't suite your needs, you should >> explicitly formulate your conceptual issue so we can provide useful advice. >> >> Cheers! >> >> .~´ >> >> On Tue, Mar 3, 2020 at 5:23 AM Alireza Kashani < >> [email protected]> wrote: >> >>> Thanks Alexander, >>> >>> May be my question was not clear enough - as you said, my data is >>> already an incident matrix. >>> I would like to use stochastic-block-models, and seems they only work >>> with adjacency matrices. Therefore, I wonder what are the possibilities to >>> work-around this problem. >>> >>> Br >>> >>> >>> On Mon, Mar 2, 2020 at 9:35 PM Alexandre Hannud Abdo < >>> [email protected]> wrote: >>> >>>> Ni! Hi Alireza, >>>> >>>> One way to work with hypergraphs is to use the incidence graph >>>> <https://en.wikipedia.org/wiki/Hypergraph> representation of the >>>> hypergraph. >>>> >>>> Abraços, >>>> ale >>>> .~´ >>>> >>>> On Mon, Mar 2, 2020 at 5:28 PM Alireza Kashani < >>>> [email protected]> wrote: >>>> >>>>> Hi, >>>>> >>>>> I wonder how one can work with hyper-graphs using graph-tool. For >>>>> example in my work, I would like to feed a data where rows are journals >>>>> and >>>>> columns are papers out-going citations. I understand such a binary matrix, >>>>> can not be used as an adjacency matrix but I am curious if it is possible >>>>> to work with this type of input. Otherwise, I appreciate to hear your >>>>> thoughts on converting this matrix into a adjacency matrix. >>>>> >>>>> Br >>>>> Alireza >>>>> _______________________________________________ >>>>> graph-tool mailing list >>>>> [email protected] >>>>> https://lists.skewed.de/mailman/listinfo/graph-tool >>>>> >>>> _______________________________________________ >>>> graph-tool mailing list >>>> [email protected] >>>> https://lists.skewed.de/mailman/listinfo/graph-tool >>>> >>> _______________________________________________ >>> graph-tool mailing list >>> [email protected] >>> https://lists.skewed.de/mailman/listinfo/graph-tool >>> >> _______________________________________________ >> graph-tool mailing list >> [email protected] >> https://lists.skewed.de/mailman/listinfo/graph-tool >> > _______________________________________________ > graph-tool mailing list > [email protected] > https://lists.skewed.de/mailman/listinfo/graph-tool >
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