18583686. METHOD AND APPARATUS FOR DETERMINING REPRESENTATION INFORMATION, DEVICE, AND STORAGE MEDIUM simplified abstract (TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED)
Contents
- 1 METHOD AND APPARATUS FOR DETERMINING REPRESENTATION INFORMATION, DEVICE, AND STORAGE MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND APPARATUS FOR DETERMINING REPRESENTATION INFORMATION, DEVICE, AND STORAGE MEDIUM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Representation Information Technology
- 1.13 Original Abstract Submitted
METHOD AND APPARATUS FOR DETERMINING REPRESENTATION INFORMATION, DEVICE, AND STORAGE MEDIUM
Organization Name
TENCENT TECHNOLOGY (SHENZHEN) COMPANY LIMITED
Inventor(s)
METHOD AND APPARATUS FOR DETERMINING REPRESENTATION INFORMATION, DEVICE, AND STORAGE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18583686 titled 'METHOD AND APPARATUS FOR DETERMINING REPRESENTATION INFORMATION, DEVICE, AND STORAGE MEDIUM
Simplified Explanation
The method described in the patent application involves using a graph neural network to determine representation information of different types of nodes in a heterogeneous graph.
- Obtaining a heterogeneous graph of a target resource service.
- Performing graph convolution on the heterogeneous graph using a graph neural network based on various meta-paths.
- Obtaining initial representation information of different types of nodes in the graph.
- Fusing the initial representation information based on the connections between nodes to obtain target representation information.
Key Features and Innovation
- Utilizes a graph neural network for determining representation information.
- Considers different types of nodes and meta-paths in the heterogeneous graph.
- Fuses representation information based on node connections.
Potential Applications
This technology can be applied in various fields such as:
- Data analysis
- Recommendation systems
- Network analysis
Problems Solved
- Efficiently determining representation information in a heterogeneous graph.
- Handling different types of nodes and connections in the graph.
Benefits
- Improved accuracy in representation information.
- Enhanced performance in analyzing complex data structures.
Commercial Applications
Title: Advanced Data Analysis Technology for Enhanced Decision Making This technology can be utilized in industries such as:
- E-commerce for personalized recommendations
- Finance for risk assessment
- Healthcare for patient diagnosis
Prior Art
Further research can be conducted in the field of graph neural networks and representation learning in heterogeneous graphs.
Frequently Updated Research
Stay updated on advancements in graph neural networks and their applications in various industries.
Questions about Representation Information Technology
How does this technology improve data analysis processes?
This technology enhances data analysis by providing accurate representation information of different nodes in a heterogeneous graph, leading to better insights and decision-making.
What are the potential applications of this technology beyond data analysis?
Apart from data analysis, this technology can be applied in recommendation systems, network analysis, and other fields requiring complex data processing.
Original Abstract Submitted
Provided are a method for determining representation information performed by a computer device. The method includes: obtaining a heterogeneous graph of a target resource service; performing graph convolution on the heterogeneous graph through a graph neural network based on a plurality of types of meta-paths of a plurality of nodes in the heterogeneous graph, to obtain initial representation information of a first-class object node and initial representation information of a second-class object node; and fusing the initial representation information of the first-class object node and the initial representation information of the second-class object node based on an edge connecting different nodes in the heterogeneous graph, to obtain target representation information of the first-class object node.