Node Similarity based Graph Visualization

Visualization is done with the MDS (Multidimensional Scaling) dimensionality reduction technique

The basis of the presented methods for the visualization and clustering of graphs is a novel similarity and distance metric, and the matrix describing the similarity of the nodes in the graph. This matrix represents the type of connections between the nodes in the graph in a compact form, thus it provides a very good starting point for both the clustering and visualization algorithms. Hence visualization is done with the MDS (Multidimensional Scaling) dimensionality reduction technique obtaining the spectral decomposition of this matrix, while the partitioning is based on the results of this step generating a hierarchical representation. A detailed example is shown to justify the capability of the described algorithms for clustering and visualization of the link structure of Web sites.

The algorithm is also desribed in:
Miklos Erdelyi, Janos Abonyi, Node Similarity-based Graph Clustering and Visualization, 7th International Symposium of Hungarian Researchers on Computational Intelligence, Budapest, Hungary, 2006.11.24-2006.11.25, Magyar Fuzzy Társaság, 2006. pp. 1-12.

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