Generate summaries of graph data for easier interpretation?
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Graph summarization is an important task for generating compact representations of graph data to facilitate easier interpretation. It helps reduce data volume, speed up query processing, and enable interactive analysis . Various approaches have been proposed for graph summarization, including techniques based on compactness of disjoint paths . These methods aim to preserve structural patterns, query answers, and specific property distributions while condensing and simplifying the graph . By analyzing the data graph using information theory, the most distinguishing features of entities can be identified and summarized in a compact and characteristic manner . Logical reasoning and similarity measurement with statistical support can further reduce information redundancy in the summaries . The use of combinatorial optimization techniques can help solve the entity summarization problem effectively .
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| Papers (5) | Insight |
|---|---|
| The paper discusses graph summarization methods that transform graphs into more compact representations while preserving structural patterns, query answers, or specific property distributions. | |
| The paper describes a method and system for generating contextual summaries of charts using statistical techniques and machine learning. | |
| The paper aims to automatically generate compact and characteristic summaries of entity descriptions in graph-structured data to present the most distinguishing features to human users. | |
| The paper proposes a graph summarization method called DJ_Paths, which aims to reduce the data volume and storage of a graph while preserving its characteristics. | |
1 Citations | The paper aims to automatically generate compact and characteristic summaries of entity descriptions in graph-structured data to present the most distinguishing features to human users. |
Related Questions
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