International business machines corporation (20240160904). GRAPH LEARNING ATTENTION MECHANISM simplified abstract
Contents
- 1 GRAPH LEARNING ATTENTION MECHANISM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GRAPH LEARNING ATTENTION MECHANISM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications of this Technology
- 1.6 Problems Solved by this Technology
- 1.7 Benefits of this Technology
- 1.8 Potential Commercial Applications of this Technology
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
GRAPH LEARNING ATTENTION MECHANISM
Organization Name
international business machines corporation
Inventor(s)
Mattson Thieme of Evanston IL (US)
ONKAR Bhardwaj of Lexington MA (US)
David Cox of Somerville MA (US)
GRAPH LEARNING ATTENTION MECHANISM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160904 titled 'GRAPH LEARNING ATTENTION MECHANISM
Simplified Explanation
The patent application describes a method for generating node representations in a graph, selecting a subgraph based on structure learning scores, and performing inferencing operations using a representation learner on the selected subgraph.
- Node representations are generated for node features in a graph.
- Structure learning scores are calculated for each edge in the graph based on the node representations.
- A subset of edges with structure learning scores above a threshold are selected to form a subgraph.
- The subgraph is inputted to a representation learner for inferencing operations.
Potential Applications of this Technology
This technology could be applied in various fields such as:
- Social network analysis
- Fraud detection systems
- Recommendation systems
Problems Solved by this Technology
- Efficient representation learning in graphs
- Automated subgraph selection for inferencing operations
Benefits of this Technology
- Improved accuracy in inferencing operations
- Scalability for large graphs
- Reduced computational complexity
Potential Commercial Applications of this Technology
Optimizing this technology for commercial applications could lead to:
- Enhanced targeted advertising strategies
- Streamlined fraud detection processes
- Personalized recommendation systems
Possible Prior Art
One possible prior art for this technology could be:
- Graph neural networks for representation learning in graphs
What are the limitations of this technology in real-world applications?
Real-world applications of this technology may face limitations such as:
- Scalability issues with extremely large graphs
- Sensitivity to noise in the data
How does this technology compare to existing methods for graph representation learning?
This technology offers advantages such as:
- Automated subgraph selection for inferencing operations
- Improved efficiency in representation learning
Original Abstract Submitted
a graph with a plurality of nodes, a plurality of edges, and a plurality of node features is obtained and node representations for the node features are generated. a plurality of structure learning scores is generated based on the node representations, each structure learning score corresponding to one of the plurality of edges. a subset of the plurality of edges that identify a subgraph is selected, each edge of the subset having a structure learning score that is greater than a given threshold. the subgraph is inputted to a representation learner and an inferencing operation is performed using the representation learner based on the subgraph.
