In this paper, we propose HyperLogic: a flexible approach leveraging hypernetworks to generate weights of the main network. HyperLogic can be combined with existing differentiable rule learning methods to generate diverse rule sets, each capable ofcapturing heterogeneous patterns indata.

WhenT is larger, more recent edges are assignedsmallDAmagnitudes,sothattheessentialsemantic information is preserved. This theorem guarantees that our DA techiniques do not break the original edge time distribution. There are 4,066 drop-out events (= 0.98%). Based on the validation results, using two TGAT layers and two attention heads with dropout rate of 0.1 gives the best performance. For inference, we inductively compute the embeddings for both the unseen and observed nodes at each time point that the graph evolves, or when the node labels are updated.

Often the setS isimplicitly represented, for example as the set of assignments that satisfy a given set of constraints.