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SimGNN:
- Encoder:
- Inputs: Initial one-hot encoded node embedding matrix
$U \in R^{NXD}$ - Outputs: Aggregated node Embedding Matrix
$U \in R^{NXD}$ - Uses: Neighbour Aggregation with Conv Nets (SAGE, GCN, GAT)
- Inputs: Initial one-hot encoded node embedding matrix
- Attention Mechanism:
- Inputs: Node Embedding Matrix
$U \in R^{NXD}$ - Outputs: Attention Weighted Graph Embedding Vector
$h \in R^{D}$ - Uses: Non linear weighted transform (
$\tanh$ ) for context, sigmoid layers for att. weights,$\sum$ aggregate for h
- Inputs: Node Embedding Matrix
- Graph Interaction Extraction:
- Inputs: Graph Embedding Vectors
$h_{q}, h_{c} \in R^{D}$ - Outputs: Interaction Score Vector
$g \in R^{K}$ , K being the depth of the NTN - Uses: Neural Tensor Network
- Inputs: Graph Embedding Vectors
- Score Predictor:
- Inputs: Graph Similarity Score Vector
$g \in R^{K}$ - Outputs: Graph Similarity Score s
- Uses: Fully Connected Network
- Inputs: Graph Similarity Score Vector
- Encoder:
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GMN Embed:
- Encoder:
- Inputs:
- Initial Node Representation Matrix
$U \in R^{NXD}$ - Initial Edge Representation Matrix
$X \in R^{NXN}$
- Initial Node Representation Matrix
- Outputs: Encoded Node and Edge Embedding Vectors
$H^{0} \in R^{NXD}$ and$E \in R^{NXN}$ - Uses: Multi Layer Perceptron Networks
- Inputs:
- Propagation:
- Inputs: Encoded embeddings
$H^{0} \in R^{NXD}$ and$E \in R^{NXN}$
- Inputs: Encoded embeddings
- Encoder: