18497571. Heterogeneous Graph Clustering Using a Pointwise Mutual Information Criterion simplified abstract (Google LLC)
Contents
- 1 Heterogeneous Graph Clustering Using a Pointwise Mutual Information Criterion
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Heterogeneous Graph Clustering Using a Pointwise Mutual Information Criterion - 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
Heterogeneous Graph Clustering Using a Pointwise Mutual Information Criterion
Organization Name
Inventor(s)
Oleg Golubitsky of Toronto (CA)
Pushkarini Hemchandra Agharkar of Toronto (CA)
Heterogeneous Graph Clustering Using a Pointwise Mutual Information Criterion - A simplified explanation of the abstract
This abstract first appeared for US patent application 18497571 titled 'Heterogeneous Graph Clustering Using a Pointwise Mutual Information Criterion
Simplified Explanation
The abstract describes a system for enforcing policies in a computer environment for content distribution using pointwise mutual information (PMI) based clustering. The system can identify attributes of assets, compute PMI scores to determine if nodes sharing attributes belong to a single content source, and cluster nodes based on predefined threshold values.
- The system maintains a network of nodes representing assets.
- Assets associated with a policy label are identified and their attributes are analyzed.
- PMI scores are computed to determine if nodes sharing attributes belong to the same content source.
- Nodes with PMI scores exceeding a predefined threshold are clustered together.
- The clusters are tagged as being associated with a specific content source related to the policy label.
Potential Applications
This technology can be applied in:
- Digital rights management systems
- Content distribution networks
- Data security platforms
Problems Solved
This technology helps in:
- Enforcing content distribution policies
- Identifying and clustering assets based on attributes
- Preventing unauthorized access to content
Benefits
The benefits of this technology include:
- Improved policy enforcement
- Enhanced content security
- Efficient asset clustering for better management
Potential Commercial Applications
Potential commercial applications include:
- Software development for content distribution
- Security solutions for digital assets
- Compliance management tools
Possible Prior Art
One possible prior art for this technology could be:
- Clustering algorithms used in data mining and machine learning applications
What are the potential scalability challenges of implementing this technology in large-scale networks?
Scalability challenges may arise in terms of:
- Processing power required for analyzing a large number of assets
- Network bandwidth for clustering nodes in real-time
How does this technology compare to existing policy enforcement systems in terms of accuracy and efficiency?
This technology offers:
- Higher accuracy in identifying content sources
- Improved efficiency in clustering assets based on attributes
Original Abstract Submitted
Systems and methods of enforcing policies in a computer environment for content distribution using pointwise mutual information (PMI) based clustering are provided. The system can maintain a network of nodes representing a plurality of assets. Upon detecting that an asset is associated with a policy label, the system can identify attributes of the asset and compute a PMI score indicating whether nodes of the network sharing the attributes belong to a single content source. Upon determining that the PMI score exceeds a predefined threshold value, the system can identify a cluster of nodes including the nodes sharing the attributes. The system can tag the cluster, for example, as being associated with a content source that is associated with the policy label.
