Deep graph representation learning
AssigneePalo Alto Research Center Incorporated
InventorsRyan Rossi, Rong Zhou
Life of this patent
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Abstract
A method of deep graph representation learning includes: deriving a set of base features; and automatically developing, by a processing device, a multi-layered hierarchical graph representation based on the set of base features, wherein each successive layer of the multi-layered hierarchical graph representation leverages an output from a previous layer to learn features of a higher-order.
Claims20
A method of deep graph representation learning, the method comprising:
- deriving a set of base features;
- adding the set of base features to a feature matrix;
- generating, by a processing device, a current feature layer from the feature matrix, the current feature layer corresponding to a set of current features;
- selecting a subset of features from the set of current features based on an evaluation of the set of current features;
- and
- adding the subset of features to the feature matrix to generate an updated feature matrix.
A system comprising:
- a memory to store a set of base features;
- and
- a processing device, operatively coupled to the memory, to:
- derive the set of base features;
- add the set of base features to a feature matrix;
- generate a current feature layer from the feature matrix, the current feature layer corresponding to a set of current features;
- select a subset of features from the set of current features based on an evaluation of the set of current features;
- and
- add the subset of features to the feature matrix to generate an updated feature matrix.
A non-transitory computer-readable storage medium having instructions stored thereon that, when executed by a processing device, cause the processing device to:
- derive a set of base features;
- add the set of base features to a feature matrix;
- generate, by the processing device, a current feature layer from the feature matrix, the current feature layer corresponding to a set of current features;
- select a subset of features from the set of current features based on an evaluation of the set of current features;
- and
- add the subset of features to the feature matrix to generate an updated feature matrix.
Dependencies are inferred from the claim text.