Unsupervised pattern discovery using dynamic graph embeddings

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.