Comments (1)
"Type-specific linear transformation" is self-explanatory. Let [64]
be the dimension of hidden states, if type A nodes are associated with feature vectors of length [8]
, then the weight matrix to linearly project type A nodes is of shape [8, 64]
.
After this projection operation, every node's vector representation (regardless of node types) is projected into the same latent vector space of shape [64].
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