Asymmetric Adjacency Matrices

[CSNoyes]

Note that if we assume the input to the E2N filter is a symmetric matrix, we can drop either the term containing the row weights, r, or the term containing the column weights, c, since the incoming and outgoing weights on each edge will be equal. In all experiments in this paper, we used E2N filters with only the |Ω| row weights in r because we did not empirically find any clear advantage in learning separate weights for both incoming and outgoing edges when training over symmetric connectome data.

Does this apply to the ann4brains library and if so could the authors lay out the necessary changes to the E2N filters to allow for asymmetric adjacency matrices?