US 12,437,193 B2
Multi-relational graph convolutional network (GCN) in risk prediction
Yada Zhu, Irvington, NY (US); Sijia Liu, Somerville, MA (US); Aparna Gupta, Latham, NY (US); Sai Radhakrishna Manikant Sarma Palepu, Troy, NY (US); Koushik Kar, Waterford, NY (US); Lucian Popa, San Jose, CA (US); Kumar Bhaskaran, Englewood Cliffs, NJ (US); and Nitin Gaur, Round Rock, TX (US)
Assigned to International Business Machines Corporation, Armonk, NY (US); and Rensselaer Polytechnic Institute, Troy, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US); and Rensselaer Polytechnic Institute, Troy, NY (US)
Filed on Apr. 27, 2021, as Appl. No. 17/241,790.
Prior Publication US 2022/0366231 A1, Nov. 17, 2022
Int. Cl. G06N 3/08 (2023.01); G06N 3/045 (2023.01)
CPC G06N 3/08 (2013.01) [G06N 3/045 (2023.01)] 18 Claims
OG exemplary drawing
 
1. A computer-implemented method of building a graph neural network to predict a risk on entities, comprising:
creating a multi-relational graph network including at least a first graph network including a first set of nodes and a first set of edges connecting at least some nodes in the first set of nodes, a second graph network including a second set of nodes and a second set of edges connecting at least some nodes in the second set of nodes, the first set of nodes and the second set of nodes representing entities, the first set of edges representing a first relationship between the entities and the second set of edges representing a second relationship between the entities;
structuring a graph convolutional network (GCN) that incorporates the multi-relational graph network;
training the GCN to predict a risk associated with a given entity,
wherein the trained GCN predicts the risk on the given entity due to climate,
wherein for each node in the first set of nodes and the second set of nodes, a node feature for the GCN includes a climate change impact indicator representing an impact of a climate change over a period on an entity represented by the node, the first set of edges represents online searches performed between the entities, and the second set of edges represents a correlation of equity returns between the entities,
wherein an edge connecting one entity and another entity in the first set of edges, represents a number of instances of directional searches performed on said one entity and said another entity immediately within a specified time period,
wherein the training including capturing interaction of climate variables and equity prices and transmission of climate risk to equity returns, the climate variables including variables corresponding to acute climate hazards and chronic climate hazards, and inputting the captured interaction of the climate variables and the equity prices and transmission of the climate risk to equity returns as values of the GCN's node feature corresponding to the climate change impact indicator;
receiving, via a user interface, a selection of the given entity whose risk due to climate is to be predicted;
in response to the receiving of the selection, running the trained GCN to predict the risk for the given entity based on the climate risk of the entities in the trained GCN; and
displaying the predicted risk for the given entity on the user interface.