Comments (1)
To take a crack at your second question: we want to add the identity matrix to our adjacency matrix in order to add self-loops onto each node. If we did not do this, at each propagation we would only be aggregating the features of each node's neighborhood without accounting for the features of the node itself. Adding self-loops allows us to aggregate a node's features with its neighbor's features (because it acts as its own neighbor).
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Related Issues (20)
- specifying modes train, validation and test HOT 1
- Where is Filter parameters in the code? HOT 1
- Hi, does pandas make the data preprocessing more simple?
- Normalization of features, batch-wise training, feature extraction
- question about the adjacency matrix HOT 2
- citeseer dataset seems doesnot work HOT 2
- Difference between TF and Pytorch version code HOT 5
- In tensor flow code you used early stopping,isn't it needed in pytorch???
- In `utils.py` line 36, wouldnt `adj = adj + (adj.T > adj)` also work? HOT 2
- Invoice node classification / meta-data extraction / single prediction with trained model
- How to do a semi-supervised learning? HOT 6
- Predicting node degree
- Question About fastmode
- Error: 'pybind11' must be installed before running the build.
- transform to other scope dataset
- Why do row normalization instead of column normalization? HOT 2
- About the dataset split HOT 3
- citeseer dataset
- Cora dataset attributes
- accuracy in the experimental results HOT 1
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