Graph Clustering with Distinct Text Summaries
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Solution Overview
Problem
Large graph structures with numerous nodes are difficult to grasp due to their size, and conventional methods of dividing them into clusters result in similar text representations, making it hard to discern differences between clusters.
Innovation Solution
An information processing apparatus that assigns nodes to clusters based on distance thresholds, classifies words into types, extracts frequent words, and generates a summarized graph structure with text representing each cluster, highlighting the subject and content of operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If the graph structure is displayed all at once, then the user can see the whole structure, but the user is unable to read the content of the graph structure
Solution Approach 1:
The graph structure is divided into multiple clusters, each representing a subset of nodes. This segmentation allows the large graph to be displayed in manageable portions while maintaining the ability to see the overall structure through the cluster arrangement.
Solution Approach 2:
Cluster representative nodes are introduced as intermediaries between the individual graph nodes and the user. These representative nodes summarize and represent the content of multiple nodes within a cluster, enabling users to grasp cluster content without reading every individual node.
2Loss of information
If only part of the oversized graph structure is displayed using enlargement, then the user can read the content, but the user is unable to grasp the full content of the graph structure
Solution Approach 1:
The graph is segmented into clusters that can be displayed at different levels of detail. Users can view the overall cluster arrangement to grasp the full structure, then focus on specific clusters for detailed content review.
Solution Approach 2:
The patent introduces a hierarchical dimension to the display, where clusters operate at one level and individual nodes operate at another. This allows simultaneous representation of both overview and detail through the cluster-node hierarchy.
3Ease of operation
If a plurality of clusters are represented by the same text, then the user can easily grasp all the content, but differences between the clusters are not discernible
Solution Approach 1:
Each cluster is assigned distinctive visual properties (colors, icons, or text labels) that reflect its specific content characteristics. This local differentiation allows users to easily distinguish between clusters while maintaining easy comprehension through the use of representative text for each cluster.
Data Source
AI summary
An information processing apparatus includes a processor. The processor receives an input of a graph structure. The graph structure has nodes including text and edge. The processor assigns the nodes to one or more clusters. The processor partitions the text into words. The processor classifies the words into 1) a word representing a subject or target of an operation, 2) a word representing a content or state of the operation, and 3) other words. The processor extracts a frequent word by counting a frequency of occurrence of one or more words classified as the words representing the subject or target of the operation and extracts a frequent word by counting a frequency of occurrence of one or more words classified as the words representing the content or state of the operation, for the respective clusters.