'What is the difference between a non-binary adjacency matrix and an edge feature matrix in graph neural network?
I have seen a paper that uses non-binary adjacency matrix to define the weights of node connections. The weights are ratios in the range [0,1]. Can these weights be also considered as edge features? Then what is the difference between having an adjacency matrix and an edge feature matrix?
Solution 1:[1]
It all depends on how you utilise this information. You can use a binary adjacency matrix to define a graph, but you can also interpret it as a fully connected graph with 0/1 features. Same with weigths in [0,1], that depending on the semantic can mean probability of observing an edge etc. (with 0 being no edge) or can be seen as a fully connected graph with float features. Depending on the choice of what you do with this interpretation you can end up with neural nets of different representational power, inductive biases etc. So unfortunately "it all depends"
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| Solution | Source |
|---|---|
| Solution 1 | lejlot |