The code implementation seems to be inconsistent with the formulas in the paper.

[gcf3711]

In the Readout phase of your paper, formulas (7) to (10) are described as follows: These formulas are implemented in lines 239 to 245 of GNNSCModel.py as follows:

        gate_input = tf.concat([last_h, self.placeholders['initial_node_representation']], axis=-1)  # [v x 2h]
        gated_outputs = tf.nn.sigmoid(regression_gate(gate_input)) * regression_transform(last_h)  # [v x 1] new_last_h

        # Sum up all nodes per graph
        graph_representations = tf.unsorted_segment_sum(data=gated_outputs,
                                                        segment_ids=self.placeholders['graph_nodes_list'],
                                                        num_segments=self.placeholders['num_graphs'])  # [g x 1]

According to the above, the variable last_h corresponds to the symbol (h_i)^T in formulas (7), the variable self.placeholders['initial_node_representation'] corresponds to the symbol (h_i)^0 in formulas (7), the variable gate_input corresponds to the symbol s_i in formulas (7), the variable regression_gate(gate_input) corresponds to the symbol g_i in formulas (8), the variable regression_transform(last_h) corresponds to the symbol o_i in formulas (9), the variable gated_ouputs should corresponds to Sigmoid(o_i * g_i) in formulas (10)

However, both formulas (8) and (9) take s_i as input, whereas the function regression_gate and regression_transform takes gate_input and last_h as input respectively, and the sigmod function has regression_gate(gate_input) ->o_i as input instead of o_i * g_i.

Is this a typo in the code implementation?

@Messi-Q