Google llc (20240289384). Local Node Embeddings for Heterogeneous Graphs simplified abstract
Contents
- 1 Local Node Embeddings for Heterogeneous Graphs
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Local Node Embeddings for Heterogeneous Graphs - 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 Heterogeneous Graph Embeddings
- 1.13 Original Abstract Submitted
Local Node Embeddings for Heterogeneous Graphs
Organization Name
Inventor(s)
Kimon Fountoulakis of Kitchener (CA)
Local Node Embeddings for Heterogeneous Graphs - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240289384 titled 'Local Node Embeddings for Heterogeneous Graphs
Simplified Explanation
The patent application describes computing systems and methods for obtaining local node embeddings for heterogeneous graphs. These systems can process graphs based on learned embeddings, improving graph analysis and processing efficiency.
- Systems and methods for obtaining local node embeddings for heterogeneous graphs
- Determining weight values associated with subgraphs of the heterogeneous graph
- Learning embeddings for selected nodes based on weight values
- Processing heterogeneous graphs based on the learned embeddings
- Using submodular hypergraphs to represent heterogeneous graphs and their cuts
Key Features and Innovation
- Obtaining local node embeddings for heterogeneous graphs
- Determining weight values for subgraphs
- Learning embeddings for selected nodes based on weight values
- Processing heterogeneous graphs using learned embeddings
- Using submodular hypergraphs for representation
Potential Applications
- Graph analysis and processing
- Network optimization
- Recommendation systems
- Social network analysis
- Bioinformatics research
Problems Solved
- Improved efficiency in processing heterogeneous graphs
- Enhanced graph analysis capabilities
- Better representation of complex network structures
- Optimal solutions for node embeddings
Benefits
- Faster graph processing
- More accurate analysis results
- Enhanced understanding of network structures
- Improved performance in various applications
Commercial Applications
- Graph database systems
- Network security solutions
- Social media platforms
- E-commerce recommendation engines
- Healthcare data analysis tools
Prior Art
Prior research in graph embedding techniques and hypergraph representation can provide insights into related technologies and approaches.
Frequently Updated Research
Stay updated on advancements in graph embedding algorithms, hypergraph theory, and network analysis methodologies for further improvements in heterogeneous graph processing.
Questions about Heterogeneous Graph Embeddings
How do heterogeneous graph embeddings improve graph analysis compared to traditional methods?
Heterogeneous graph embeddings provide a more nuanced understanding of complex network structures, allowing for more accurate analysis and predictions.
What are the potential challenges in implementing heterogeneous graph embeddings in real-world applications?
Challenges may include scalability issues with large graphs, optimizing embedding algorithms for specific use cases, and ensuring the accuracy of learned embeddings in dynamic network environments.
Original Abstract Submitted
provided are computing systems, methods, and platforms that obtain local node embeddings for heterogeneous graphs. a heterogeneous graph comprising a plurality of nodes can be obtained. weight values respectively associated with subgraphs of the heterogeneous graph can be determined. at least one node from among the plurality of nodes can be selected. an embedding for the at least one selected node can be learned using an embedding objective computed based on the weight values. the heterogeneous graph can be processed based on the embedding. submodular hypergraphs can be used to represent heterogeneous graphs and their cuts. the -regularized personalized pagerank can be applied to hypergraphs, where the optimal solution gives the node embedding for the given seed nodes. the resulting -regularized personalized pagerank can be solved in running time without depending on the size of the whole graph.
