MIL has become particularly popular in histopathology, where gigapixel microscopy slides are cut into patches representing small tissue regions.

As defined inthe Introduction Section 1ofthe main paper,this12 graphlet sequence encapsulates the temporal evolution of node pair relationships.

Then, the key is to capture the temporal evolution of node pair (term pair) relations from just the positive and unlabeled data. We propose a variational inference model to estimate the positive prior, and incorporate it in the learning of node pairembeddings, which arethenused forlinkprediction.

However, G-CNNs are faced withtwomajorchallenges: spatial-agnosticproblem andexpensivecomputational cost. However, it is essentially G-CNNs which still have the inherent spatial-agnostic problem.

Infederated learning, itiscommon toassume thatclients arealwaysavailable to participate in training, which may not be feasible with user devices in practice.