17979817. GRAPH LEARNING ATTENTION MECHANISM simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)
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
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 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 17979817 titled 'GRAPH LEARNING ATTENTION MECHANISM
Simplified Explanation
The patent application describes a method for generating node representations in a graph, selecting a subset of edges based on structure learning scores, inputting the subset into a representation learner, and performing inferencing operations.
- Node representations are generated for node features in a graph.
- Structure learning scores are calculated for each edge in the graph.
- A subset of edges with structure learning scores above a threshold is selected to form a subgraph.
- The subgraph is inputted into a representation learner for inferencing operations.
Potential Applications
This technology could be applied in various fields such as social network analysis, recommendation systems, and bioinformatics for analyzing complex relationships and making predictions based on subgraph structures.
Problems Solved
This technology helps in efficiently identifying important subgraphs in large graphs, which can be used for making accurate predictions and in-depth analysis of relationships within the data.
Benefits
The method allows for better understanding of complex graph structures, leading to improved decision-making and predictive accuracy in various applications.
Potential Commercial Applications
- "Graph Subgraph Selection for Enhanced Inferencing" - This technology can be utilized in industries such as finance, healthcare, and e-commerce for optimizing recommendations, fraud detection, and personalized services based on graph data analysis.
Possible Prior Art
There may be prior art related to graph subgraph selection and representation learning techniques in the field of machine learning and data analysis.
Unanswered Questions
How does this technology compare to existing methods for subgraph selection and inferencing in graphs?
This article does not provide a direct comparison with existing methods in the field, leaving the reader to wonder about the specific advantages and limitations of this approach.
What are the potential limitations or challenges in implementing this technology in real-world applications?
The article does not address the practical considerations or potential obstacles that may arise when applying this technology in different industries or scenarios.
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.
