18052463. SYSTEMS AND METHODS FOR CONTRASTIVE GRAPHING simplified abstract (ADOBE INC.)
Contents
- 1 SYSTEMS AND METHODS FOR CONTRASTIVE GRAPHING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR CONTRASTIVE GRAPHING - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
SYSTEMS AND METHODS FOR CONTRASTIVE GRAPHING
Organization Name
Inventor(s)
Namyong Park of Pittsburgh PA (US)
Ryan A. Rossi of San Jose CA (US)
Eunyee Koh of Sunnyvale CA (US)
Iftikhar Ahamath Burhanuddin of Bangalore (IN)
Sungchul Kim of San Jose CA (US)
SYSTEMS AND METHODS FOR CONTRASTIVE GRAPHING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18052463 titled 'SYSTEMS AND METHODS FOR CONTRASTIVE GRAPHING
Simplified Explanation
The abstract of this patent application describes systems and methods for contrastive graphing, which involves receiving a graph with a node, generating a node embedding using a graph neural network (GNN), computing a contrastive learning loss based on the node embedding, and updating GNN parameters based on the contrastive learning loss.
- Graph neural network (GNN) is used to generate node embeddings based on the input graph.
- A contrastive learning loss is computed based on the node embeddings to improve the performance of the GNN.
- Parameters of the GNN are updated using the contrastive learning loss to enhance the graph representation capabilities.
Potential Applications
This technology can be applied in various fields such as:
- Social network analysis
- Recommendation systems
- Bioinformatics
Problems Solved
The technology addresses the following issues:
- Improving graph representation learning
- Enhancing the performance of graph neural networks
- Facilitating better understanding of complex relationships in graphs
Benefits
The benefits of this technology include:
- More accurate node embeddings
- Enhanced graph analysis capabilities
- Improved performance of machine learning models on graph data
Potential Commercial Applications
A potential commercial application for this technology could be:
- Developing advanced recommendation systems for e-commerce platforms
Possible Prior Art
One possible prior art related to this technology is the use of graph neural networks for node embedding generation in graph data analysis.
Unanswered Questions
How does this technology compare to traditional graph embedding methods?
This article does not provide a direct comparison between this technology and traditional graph embedding methods.
What are the limitations of using contrastive learning for graph representation?
The article does not discuss any potential limitations or challenges associated with using contrastive learning for graph representation.
Original Abstract Submitted
Systems and methods for contrastive graphing are provided. One aspect of the systems and methods includes receiving a graph including a node; generating a node embedding for the node based on the graph using a graph neural network (GNN); computing a contrastive learning loss based on the node embedding; and updating parameters of the GNN based on the contrastive learning loss.
