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US 2021/0232918 A1Published application
Node aggregation with graph neural networks
AssigneeNEC Laboratories America, Inc.
InventorsWei Cheng, Haifeng Chen, Wenchao Yu
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Abstract
Methods and systems for training a graph neural network (GNN) include training a denoising network in a GNN model, which generates a subgraph of an input graph by removing at least one edge of the input graph. At least one GNN layer in the GNN model, which performs a GNN task on the subgraph, is jointly trained with the denoising network.
Claims20
A computer-implemented method for training a graph neural network (GNN), comprising:
- training a denoising network in a GNN model, which generates a subgraph of an input graph by removing at least one edge of the input graph using a low-rank constraint;
- and
- training at least one GNN layer in the GNN model, which performs a GNN task on the subgraph, jointly with the denoising network.
A system for training a graph neural network (GNN), comprising:
- a hardware processor;
- and
- a memory that stores computer program code, which, when executed by the processor, implements:
- a GNN model that includes a denoising network, which generates a subgraph of an input graph by removing at least one edge of the input graph using a low-rank constraint, and at least one GNN layer, which performs a GNN task on the subgraph;
- and
- a model trainer that trains the GNN model using a training data set.
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