Graph encoder Both tasks were tackled in tandem with a mapping function f : G M → G ext , modeled as a GNN. To mitigate the over-smoothing problem, we used multiple aggregation functions through the message-passing scheme, along with residual connections. The encoder is constructed by stacking several propagation layers followed by a ReLU activation function. Node embeddings are then concatenated with a residual connection from the input graph. The result is forwarded to a multilayer perceptron (MLP), which in this case, is composed of two linear layers separated by a ReLU activation layer.