Social Content Visualization via Multi-Attribute Clustering
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Solution Overview
Problem
Conventional social network analysis systems struggle to effectively represent and process additional attributes or features beyond binary or ordinal relationships between individuals, limiting their ability to provide comprehensive insights.
Innovation Solution
The system determines a corpus of content portions, calculates feature values, clusters them based on these values, and uses structural links to inform a layout that visually represents relationships, incorporating dominant topics to enhance the display and visualization of social and textual content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional social network analysis tools are used to represent relationships between individuals, then binary or ordinal valued relationships can be displayed, but additional attributes or features beyond these basic relationships cannot be effectively represented or processed
Solution Approach 1:
The patent transitions from conventional two-dimensional graph representations to a multi-dimensional visualization system where nodes are positioned in a higher-dimensional space based on multiple attributes simultaneously. This allows binary relationships, ordinal relationships, and additional attributes to be represented concurrently without increasing apparent system complexity, as the extra dimensions are projected into the visual space through sophisticated layout algorithms.
Solution Approach 2:
The visualization system is designed to handle multiple types of relationships and attributes through a unified framework. The same graph structure and layout engine can process binary relationships, ordinal relationships, and additional attributes simultaneously, making the system versatile without requiring separate tools for each relationship type.
2Loss of information
If multiple attributes and features are incorporated into the analysis, then more comprehensive insights can be obtained, but the difficulty of processing and representing this information increases
Solution Approach 1:
The patent segments the multiple attributes and features into distinct relationship types (binary relationships, ordinal relationships, and additional attributes) that can be processed separately by dedicated calculation modules. Each module handles one type of attribute, reducing the overall processing difficulty while preserving all information in the final integrated visualization.
Solution Approach 2:
The patent introduces layout algorithms as intermediary processing steps that translate multiple attributes into spatial positions. These algorithms act as mediators between the raw attribute data and the final visual representation, automatically handling the complexity of processing multiple features while preserving their informational content in the node positions and connections.
Data Source
AI summary
Techniques are presented for determining a corpus of content portions, each content portion associated with at least one element. A first set of feature values is determined for each content portion. Clusters of content portions are then determined based on the first set of feature values. The features values are optionally associated with topics. Structural links between the elements are determined based on a second set of feature values. A layout of the element is then determined based on the clusters and the structural links. Optionally the N-most dominant topics are determined and also used to inform the layout of the elements in a display.