Abstract: Methods and processors are disclosed. The method includes acquiring an input indicative of a Relational Question-Answering (RQA) problem, generating an instance graph based on the input, generating an intermediate output indicative of one or more reasoning paths in the instance graph and generating, based on the input and the intermediate output, an output indicative of one or more potential answers to the RQA problem.

Abstract: System, method, and computer readable medium for operating a computer system to process a graph, including receiving a prediction request for a subject node of the graph; obtaining a sparse node approximation for the subject node, the sparse node approximation defining a weighted combination of a subset of nodes of the graph as receptive nodes for the subject node; applying a neural network based transformation function based on the sparse node approximation to generate a node representation for the subject node; performing a prediction task based on the generated node representation to generate a prediction for the subject node; and outputting the prediction.

Abstract: Systems and methods for selecting one or more characteristics for a netlist. A graph data structure is generated, the components of the netlist and connections therebetween being represented by nodes and edges, respectively, in the graph data structure, wherein the edges between the nodes indicate types of the connections between the components. The graph data structure is processed to select one or more characteristics for the components.

Abstract: Systems, methods, and computer-readable media provide a graph processing system that incorporates a graph neural network (GNN) based recommender system (RS), as well as a method for training a GNN based RS to address feature leakage that leads to overfitting of the trained GNN based RS. A message correction algorithm is used to modify a user node embedding and a positive item node embedding generated by the graph neural network when generating mini batches of training triples used to train the GNN based RS. The GNN message passing operations are performed on one graph only, in contrast to existing approaches which typically run GNN message passing operations on multiple adjusted input graphs constructed for multiple training triples.