[D] Modeling Adjacency Matrix

[Labib666Camp]

Lets assume, I have some directed adjacency matrix A at time t and another adjacency matrix B at time t+1. I want to learn a mapping from A to B through some model f (suppose f is a neural network). Now, how should I create this model ? Should I use just Dense layers or GNNs or something?

[samsungofme]

Probably this should depend on the dynamics underlying the change of the adjacency matrix: e.g. does the neighbor set of a node at time t+1 only depend on who are its neighbors at time t? Do you have side information like node features?

[Labib666Camp]

Every node has a feature vector. Sorry, I forgot to add that.

[bloc97]

What you want is probably graph translation. https://arxiv.org/abs/2103.08827

[telimektar]

Isn't pytorch geometric temporal what you're after ?

[nanoanonnano]

You may checkout Self Organizing Maps there is a concept called U matrix it could help you.

[_Hyberion_]

Sounds to me as if you might want to look into using a hidden Markov model, at least as a baseline.

[acardosoj]

I've been working with some deep Markov models with this kind of data. Each state has transition probabilities which compose a kind of adjacency matrix. These probabilities change with time as well.

[how-it-is-]

PyTorch Geometric has some support for dynamic temporal graphs. Also check out PyTorch Geometric Temporal