Comments (3)
Hello,
Thanks for your interest in our work!
We make the node ids and edge ids start from 1 because we use the node/edge with id 0 as the mask node/edge, which is easy to implement. Their features are also set to zero vectors. The mask node/edge is then used for padding by the dynamic graph learning methods.
For example, we pad the neighbors in NeighborSampler
here and pad the edges here. Then, the dynamic graph learning methods perform masked computations. For instance, the causal attention here.
I hope this answer has well addressed your issue. And feel free to ask if there are any further questions.
Best,
Le Yu
from dyglib.
Thanks for your swift response :) I am actually still confused. I think I need some time to compare the CTDG implementations from PyG, DyGLib, and those official repos so that I can have a comprehensive understanding.
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I hope you are doing well. And welcome to contact me if there are any issues with our DyGLib.
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Related Issues (12)
- A question about dynamic node features HOT 2
- Creating custom dynamic and temporal dataset for link prediction HOT 3
- Question of dynamic node classification & edge classification? HOT 4
- TypeError:'float!object cannot be interpreted as an integer HOT 1
- Questions about the application of the so-called Patching Technique HOT 2
- time_interval_aware策略下计算采样概率为什么用的np.cumsum HOT 2
- Inference code HOT 1
- Adapting Models for Node Classification Tasks to Datasets Lacking Edge Features HOT 3
- Request for Guidance: Node Label Coverage Issue in Dataset Processing for Node Classification Task HOT 6
- Questions about how to construt the initial <ml_network.csv> file for node classification task HOT 11
- Questions towards the details of paper and code
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