Graph representations
AssigneeFujitsu Limited
InventorsArnab Kumar MONDAL, Jay Nandy
Life of this patent
Estimating the term…
Abstract
A computer-implemented method comprising performing a training process, the training process comprising: using a student graph neural network, GNN, to extract a pair of first representations from a pair of training graphs, respectively; computing a disagreement between the first representations; using at least one teacher GNN to extract a pair of second representations from the pair of training graphs, respectively; computing a perceptual distance between the second representations; comparing the disagreement between the first representations with a target disagreement which is based at least in part on the perceptual distance between the second representations; and adjusting at least one weight of the student GNN based on the comparison.
Claims20
A computer-implemented method comprising performing a training process, the training process comprising:
- using a student graph neural network, GNN, to extract a pair of first representations from a pair of training graphs, respectively;
- computing a disagreement between the first representations;
- using at least one teacher GNN to extract a pair of second representations from the pair of training graphs, respectively;
- computing a perceptual distance between the second representations;
- comparing the disagreement between the first representations with a target disagreement which is based at least in part on the perceptual distance between the second representations;
- and
- adjusting at least one weight of the student GNN based on the comparison.
A computer program which, when run on a computer, causes the computer to carry out a method comprising performing a training process, the training process comprising:
- using a student graph neural network, GNN, to extract a pair of first representations from a pair of training graphs, respectively;
- computing a disagreement between the first representations;
- using at least one teacher GNN to extract a pair of second representations from the pair of training graphs, respectively;
- computing a perceptual distance between the second representations;
- comparing the disagreement between the first representations with a target disagreement which is based at least in part on the perceptual distance between the second representations;
- and
- adjusting at least one weight of the student GNN based on the comparison.
An information processing apparatus comprising a memory and a processor connected to the memory, wherein the processor is configured to perform a training process, the training process comprising:
- using a student graph neural network, GNN, to extract a pair of first representations from a pair of training graphs, respectively;
- computing a disagreement between the first representations;
- using at least one teacher GNN to extract a pair of second representations from the pair of training graphs, respectively;
- computing a perceptual distance between the second representations;
- comparing the disagreement between the first representations with a target disagreement which is based at least in part on the perceptual distance between the second representations;
- and
- adjusting at least one weight of the student GNN based on the comparison.
Dependencies are inferred from the claim text.