Calculation of local smoothing iteration

[sungeun532]

In train.py, when you calculate the LSI, why do you not calculate node's converget state for each node, but calculate 1x(dim of feat) vector "norm_fea_inf" as the same convergent stae for all nodes?

[sungeun532]

and,, how do you prove the smoothed normalized adjacecncy matrix in equation (2) ?? I really try to prove it... it is so hard, plz help me,,

[sungeun532]

In train.py, when you calculate the LSI, why do you not calculate node's converget state for each node, but calculate 1x(dim of feat) vector "norm_fea_inf" as the same convergent stae for all nodes?

I understand this (since random walk stationary distribution is only dependent on the target node, right?)
the only thing that i want is specific proof of general stationary adjacency matrix in equation(2),,
I understand to a certain extent, but I want to know for general normalized factor r.