Task-guided graph augmentation and editing for node classification and fraud detection
AssigneesInternational Business Machines Corporation, Massachusetts Institute of Technology
InventorsYada Zhu, Xiaojie Guo, David Cox, Shuaicheng Zhang
Life of this patent
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Abstract
A computer-implemented method for task-guided graph augmentation and editing includes receiving an input graph in an observed financial transaction network. A data augmentation function is learned, where the data augmentation function maintains a true data distribution of the input graph. An augmented financial transaction network is generated that enhances performance of a downstream task and preserves topological and temporal properties of the input graph.
Claims20
A computer-implemented method for task-guided graph augmentation and editing, comprising:
- receiving an input graph in an observed financial transaction network;
- learning a data augmentation function that maintains a true data distribution of the input graph;
- and
- generating an augmented financial transaction network that enhances performance of a downstream task and preserves topological and temporal properties of the input graph.
A system comprising:
- a processor;
- a data bus coupled to the processor;
- a memory coupled to the data bus;
- and
- a computer-usable medium embodying a computer program code, the computer program code comprising instructions executable by the processor and configured to:
- receive an input graph in an observed financial transaction network;
- learn a data augmentation function that maintains a true data distribution of the input graph;
- and
- generate an augmented financial transaction network that enhances performance of a downstream task and preserves topological and temporal properties of the input graph.
A computer program product for task-guided graph augmentation and editing, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:
- receive an input graph in an observed financial transaction network;
- learn a data augmentation function that maintains a true data distribution of the input graph;
- and
- generate an augmented financial transaction network that enhances performance of a downstream task and preserves topological and temporal properties of the input graph.
Dependencies are inferred from the claim text.