How to build a model to predict a graph (not a image) in time series?
There is an adjacent matrix dataset that is based on time series. I would like to know if it is possible to build a neural network model to predict tn time point's matrix by using the previous time-series data. In my opinion, traditional models such as CNN may not fit for the sparse matrix graph. 

Maybe you should give a look at Graph Neural Networks (specialy Spatial-Temporal Graph Networks). They use temporal information about graphs and its adjacency matrix to predict future nodes states, such values in the next-step. 

You can read this survey paper as a start point and follow its cited works therefore.
