Hierarchical clustering for graph visualization

Abstract

This paper describes a graph visualization methodology based on hierarchical maximal modularity clustering, with interactive and significant coarsening and refining possibilities. An application of this method to HIV epidemic analysis in Cuba is outlined.

6 pages

References in corpus (5)

• The structure and function of complex networks
• Community detection in graphs
• Modularity clustering is force-directed layout
• Partitioning and modularity of graphs with arbitrary degree distribution
• Optimizing an Organized Modularity Measure for Topographic Graph Clustering: a Deterministic Annealing Approach

Cited by in corpus (2)

• Graph Clustering with Graph Neural Networks
• Persistent Homology Guided Force-Directed Graph Layouts