Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

Reducing redundant information to find simplifying patterns in data sets and complex networks is a scientific challenge in many knowledge fields. Moreover, detecting the dimensionality of the data is ...

SM-GNN prunes multi-view GNNs to pure propagation, cutting training time while outperforming prior MKGC accuracies on two ...

With a $9.2 million grant from Intelligence Advanced Research Projects Activity (IARPA), Prof. Andrew A. Chien will lead a team of University of Chicago computer science researchers building the ...

Researchers at Shanghai Jiao Tong University have made a groundbreaking discovery in the field of Temporal Knowledge Graphs ...

As the sources, types, and amounts of data continue to expand, so will the need for different kinds of analytics to make something of that data. Unfortunately, there is not a one-size-fits-all ...