Visual Analysis of Large Graphs Using (X,Y)-Clustering and Hybrid Visualizations

Area of Science:

• Computer Science
• Data Visualization
• Network Analysis

Background:

• Visual analysis of large networks is challenging.
• Clustering offers a promising approach for network visualization.
• Existing methods may not adequately preserve topological properties.

Purpose of the Study:

• To propose a new clustering technique for network visualization.
• To formalize a framework ensuring desired topological properties for both intracluster and intercluster graphs.
• To enable hybrid visualization tools for interactive graph exploration.

Main Methods:

• Developed the (X,Y)-clustering framework.
• Defined classes X and Y for intercluster and intracluster graph properties, respectively.
• Implemented the Visual Hybrid (X,Y)-clustering (VHYXY) system.

Main Results:

• The (X,Y)-clustering framework successfully generates graphs with desired topological properties.
• The VHYXY system facilitates interactive exploration of networks.
• Case studies on social network analysis demonstrate the approach's effectiveness.

Conclusions:

• The proposed (X,Y)-clustering framework enhances network visualization by controlling topological properties.
• Hybrid visualization tools leveraging this framework improve user interaction and mental map preservation.
• This approach offers a robust solution for visually analyzing complex social networks.