International Business Machines Corporation

Rensselaer Polytechnic Institute

Sai Radhakrishna Manikant Sarma Palepu

Nitin Gaur

Yada Zhu

Lucian Popa

Sijia Liu

Kumar Bhaskaran

Koushik Kar

Aparna Gupta

A graph neural network can be built and trained to predict a risk of an entity. A multi-relational graph network can include a first graph network and a second graph network. The first graph network can include a first set of nodes and a first set of edges connecting some of the nodes in the first set. The second graph network can include a second set of nodes and a second set of edges connecting some of the nodes in the second set. The first set of nodes and the second set of nodes can represent entities, the first set of edges can represent a first relationship between the entities and the second set of edges can represent a second relationship between the entities. A graph convolutional network (GCN) can be structured to incorporate the multi-relational graph network, and trained to predict a risk associated with a given entity.

Multi-relational graph convolutional network (GCN) in risk prediction

Growing community of inventors

Troy, NY, United States of America

Sai Radhakrishna Manikant Sarma Palepu