Abstract: Discussed herein are devices, systems, and methods for unsupervised pattern discovery using continuous-time dynamic graphs. A method can include receiving, from a graph neural network (GNN), source node embeddings and destination node embeddings, clustering the destination node embeddings generated by the GNN resulting in first groups of destination node embeddings, removing, from the destination node embeddings, embeddings from a noise group of the first groups resulting in signal destination node embeddings, clustering the signal destination node embeddings resulting in second groups of destination node embeddings, and identifying a pattern in the destination node embeddings and source node embeddings based on the second groups of destination node embeddings, the source node embeddings, and the destination node embeddings.

Abstract: Discussed herein are devices, systems, and methods for more flexible temporal graph network (TGN) graph interaction. A method includes executing first and second temporal graph networks (TGNs) to generate embeddings of respective first and second dynamic graphs, storing, as respective edge features of a first node of the first graph and a second node of the second graph, a memory state vector of the first node and a memory state vector of the second node, and determining, based on the embeddings and the edge features, a likelihood of an edge between nodes of the first graph.

Abstract: A multi-modal natural language question answering system and method comprises receiving a question logic form, at least one answer logic form, and utilizing semantic relations, contextual information, and adaptable logic.