mot_neural_solver
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Why using a directed graph in the code?
Hello!
In the paper it is stated that the problem is modelled with an undirected graph, however, while checking the code I saw the graph is computed as follows:
self.graph_obj = Graph(x = node_feats, edge_attr = torch.cat((edge_feats, edge_feats), dim = 0),
edge_index = torch.cat((edge_ixs, torch.stack((edge_ixs[1], edge_ixs[0]))), dim=1))
As far as I understand the edges' connections (edge_index) are computed by concatenating all the previously computed edges but in the opposite direction, i.e. creating a directed graph. Am I correct?
Could you please tell me the differences in performance and/or the advantages of using a directed graph? Thank you in advance