PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Visualizing very large graphs using clustering neighborhoods
Dunja Mladenić and Marko Grobelnik
In: Local pattern detection : international seminar : Dagstuhl Castle Lecture notes in computer science : Lecture notes in artificial intelligence , 3539 . (2005) Springer , Berlin; Heidelberg; New York: , pp. 89-97.


This paper presents a method for visualization of large graphs in a two-dimensional space, such as a collection of Web pages. The main contribution here is in the representation change to enable better handling of the data. The idea of the method consists from three major steps: (1) First, we transform a graph into a sparse matrix, where for each vertex in the graph there is one sparse vector in the matrix. Sparse vectors have non-zero components for the vertices that are close to the vertex represented by the vector. (2) Next, we perform hierarchical clustering (eg., hierarchical K-Means) on the set of sparse vectors, resulting in the hierarchy of clusters. (3) In the last step, we map hierarchy of clusters into a two-dimensional space in the way that more similar clusters appear closely on the picture. The effect of the whole procedure is that we assign unique X and Y coordinates to each vertex, in a way those vertices or groups of vertices on several levels of hierarchy that are stronger connected in a graph are place closer in the picture. The method is particular useful for power distributed graphs. We show applications of the method on real-world examples of visualization of institution collaboration graph and cross-sell recommendation graph.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:1416
Deposited By:Dunja Mladenić
Deposited On:28 November 2005