I checked out applications of GraphSAGE and it seems like its primarily used for single graph datasets. For example - Cora dataset - Its one big graph with 2708 nodes and 5429 edges. The model can learn the node representation of this big graph and it can be later used for downstream tasks like node classification, link prediction.
I have a dataset with hundreds of graphs that are relatively small (about 15 nodes and 20 edges on avg per graph). Each graph represents a separate datapoint i.e, there is no relation between these graphs. It is similar to datasets like MUTAG, PROTEIN datasets . My question is if GraphSAGE is suitable for this kind of data ? Can it be used to learn node representation and be used for downstream tasks like node/graph/link classification?
I am following this stellargraph example - stellargraph_graphsage It uses a sampler to perform random walks and solve a classification task. However, this sampler intakes only one Graph as input (big Cora graph). I am confused about how to use a dataset with multiple graphs here.
unsupervised_samples = UnsupervisedSampler( G, nodes=nodes, length=length, number_of_walks=number_of_walks )