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TS-Extractor: large graph exploration via subgraph extraction based on topological and semantic information
Kun Fu1,2,3,4, Tingyun Mao1,2,3,4, Yang Wang1,2
1Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094 China.
Journal of Visualization
|September 28, 2020
Summary
TS-Extractor enhances large graph exploration by extracting relevant subgraphs. This approach combines graph topology and node attributes for clearer semantic understanding and user-guided analysis.
Area of Science:
- Computer Science
- Data Visualization
- Graph Theory
Background:
- Exploring large graphs is challenging due to size and complex semantic information.
- Existing subgraph extraction methods often overlook node attributes, leading to unclear semantics.
Purpose of the Study:
- To propose TS-Extractor, a novel approach for extracting semantically rich subgraphs from large graphs.
- To enable local graph exploration from user-selected focus nodes.
Main Methods:
- TS-Extractor combines graph topology with user-selected node attributes.
- It extracts and visualizes connected subgraphs prioritizing nodes with similar attributes to focus nodes.
- A web-based system facilitates interactive subgraph extraction, analysis, and expansion.
Main Results:
- The approach successfully extracts subgraphs with clear semantics by integrating topological and attribute information.
- Case studies and a user study demonstrated the usability and effectiveness of TS-Extractor.
Conclusions:
- TS-Extractor provides an effective method for exploring large graphs from a local perspective.
- The integration of node attributes significantly improves the semantic clarity of extracted subgraphs.
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