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This repository has been archived by the owner on Nov 12, 2021. It is now read-only.
As I understand, there is not mistake conceptually. In paper $z$ learns how much information save, but in code it is responsible how much information forget. Am I right?
As I understand, there is not mistake conceptually. In paper $z$ learns how much information save, but in code it is responsible how much information forget. Am I right?
the readability of the code in this repository is too bad.
In original paper the last rule of update node vector is:$v_i^t = (1 - z_{s, i}^t) \odot v_i^{t-1} + z_{s, I}^t \odot v_i^t$ . But following the code, the rules is not the same:$v_i^t = (1 - z_{s, i}^t) \odot v_i^t + z_{s, I}^t \odot v_i^{t-1}$ . The difference is swap v_i^t and v_i^{t-1}.
https://github.com/CRIPAC-DIG/SR-GNN/blob/90123c88850eec8c574518fee6e46aefb42acb94/pytorch_code/model.py#L45-L47
Code's rule is:
Is it mistake in paper or in code?
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