May I know how you generate candidate adjacency matrices?
Suppose the graph contains N different edge types. Will you generate N different candidate adjacency matrices while each candidate adjacency matrix denotes the adjacency information of that edge type?
Besides, if the graph is large, the adjacency matrix should be large. It is impossible to store the whole matrix in memory. When you train the model, do you use message passing instead of matrix multiplication?
May I know how you generate candidate adjacency matrices? Suppose the graph contains N different edge types. Will you generate N different candidate adjacency matrices while each candidate adjacency matrix denotes the adjacency information of that edge type?
Besides, if the graph is large, the adjacency matrix should be large. It is impossible to store the whole matrix in memory. When you train the model, do you use message passing instead of matrix multiplication?