Conference Paper

# A Space Efficient Clustered Visualization of Large Graphs

Univ. of Technol., Sydney

DOI: 10.1109/ICIG.2007.10 Conference: Image and Graphics, 2007. ICIG 2007. Fourth International Conference on Source: IEEE Xplore

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**ABSTRACT:**Many genomic and proteomic analyses generate as a result a tree of genes or proteins. These trees are often large (containing tens of thousands of nodes and edges), and need a visualization tool to fully display all the information contained in the tree. Clustering analysis can be performed on these trees to obtain clusters of proteins, and we need an efficient way to visualize the clustering results. We present a novel tree visualization tool to help with such analyses. AVAILABILITY: http://www2.renci.org/~jeff/software/bin/win32/ProteinVis-2.1.6-win32.zip.Bioinformatics 02/2009; 25(4):557-8. · 5.47 Impact Factor -
##### Article: Fast layout computation of clustered networks: Algorithmic advances and experimental analysis

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**ABSTRACT:**The visual analysis of large and complex relational data sets is a growing need in many application domains, such as social sciences, biology, computer networks, and software engineering. In this respect, the capability of quickly computing two-dimensional layouts of hierarchically clustered networks plays an important role and should be a major requirement of many graph visualization systems. We present algorithmic and experimental advances on the subject, namely: (i) we propose a new drawing algorithm that combines space-filling and fast force-directed methods; it runs in O(n log n + m) time, where n and m are the number of vertices and edges of the network, respectively. This running time does not depend on the number of clusters, thus the algorithm guarantees good time performance independently of the structure of the cluster hierarchy. As a further advantage, the algorithm can be easily parallelized. (ii) We discuss the results of an experimental analysis aimed at understanding which clustering algorithms can be used in combination with our visualization technique to generate better quality drawings for small-world and scale-free networks of medium and large size. As far as we know, no previous similar experiments have been done to this aim.Information Sciences. 03/2014; 260:185–199. - [Show abstract] [Hide abstract]

**ABSTRACT:**Current applications have produced graphs on the order of hundreds of thousands of nodes and millions of edges. To take advantage of such graphs, one must be able to find patterns, outliers and communities. These tasks are better performed in an interactive environment, where human expertise can guide the process. For large graphs, though, there are some challenges: the excessive processing requirements are prohibitive, and drawing hundred-thousand nodes results in cluttered images hard to comprehend. To cope with these problems, we propose an innovative framework suited for any kind of tree-like graph visual design. GMine integrates (a) a representation for graphs organized as hierarchies of partitions - the concepts of SuperGraph and Graph-Tree; and (b) a graph summarization methodology - CEPS. Our graph representation deals with the problem of tracing the connection aspects of a graph hierarchy with sublinear complexity, allowing one to grasp the neighborhood of a single node or of a group of nodes in a single click. Once a neighborhood is found, GMine provides interactive mining capabilities permitted by the summarization of its nodes through efficient algebraic processing. As a proof of concept, the visual environment of GMine is instantiated as a system in which large graphs can be investigated globally and locally.IEEE Transactions on Knowledge and Data Engineering 01/2011; · 1.89 Impact Factor

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