This is the Group Work for Machine Learning Course.

• paper: Are we really making much progress? Revisiting, benchmarking,and refining heterogeneous graph neural networks

• The model extend the original graph attention mechanism in GAT by including edge type information into attention calculation.
• At each layer, we calculate the coefficient:

$$ \alpha_{ij} = \frac{exp(LeakyReLU(a^T[Wh_i||Wh_j||W_r r_{\psi(<i,j>)}]))}{\Sigma_{k\in\mathcal{E}}{exp(LeakyReLU(a^T[Wh_i||Wh_k||W_r r_{\psi(<i,k>)}]))}} $$

• Residual connection including Node residual

$$ h_i^{(l)} = \sigma(\Sigma_{j\in \mathcal{N}i} {\alpha{ij}^{(l)}W^{(l)}h_j^{(l-1)}} + h_i^{(l-1)}) $$

• where $h_i$ and $h_j$ is the features of the source and the target node. $r_{\psi(e)}$ is a $d$-dimension embedding for each edge type $\psi(e) \in T_e$.

• and Edge residual:

$$ \alpha_{ij}^{(l)} = (1-\beta)\alpha_{ij}^{(l)}+\beta\alpha_{ij}^{(l-1)} $$

• Finally, a multi-head attention is used.

• Python >= 3.6
• Pytorch >= 1.9.0
• DGL >= 0.8.0

python trainer.py --model SimpleHGN --dataset acm4GTN --n_epoch 200 --num_heads 4 --in_dim 256 --edge_dim 64 --hidden_dim 128 --out_dim 64 --num_layers 2 --feat_drop 0.2 --negative_slope 0.2 --beta 0.2 --clip 1.0 --max_lr 1e-3