Graph neural networks for datasets with heterophily
US12175366B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 23, 2021 |
| Grant date | Dec 24, 2024 |
| Priority date | — |
| Expiry date | Oct 26, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/02
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are provided for training graph neural networks with heterophily datasets and generating predictions for such datasets with heterophily. A computing device receives a dataset including a graph data structure and processes the dataset using a graph neural network. The graph neural network defines prior belief vectors respectively corresponding to nodes of the graph data structure, executes a compatibility-guided propagation from the set of prior belief vectors and using a compatibility matrix. The graph neural network predicts predicting a class label for a node of the graph data structure based on the compatibility-guided propagations and a characteristic of at least one node within a neighborhood of the node. The computing device outputs the graph data structure where it is usable by a software tool for modifying an operation of a computing environment.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.