Customization and usability study of general purpose software tools

The ability of drawing very large networks as e.g. large computer networks is of great significance in visualizing the evolution of stochastic models for evolving networks. One focuses on designing and implementing new algorithms and innovative software systems that display a large graph at different abstraction levels. For example, there is an increasing need of systems that show maps of the Web and support the user during her navigation, of systems that display and monitor the traffic on the Internet, and of systems that draw portions of the Internet as a graph.

Until now, the vast majority of graph drawing algorithms that have been deeply studied and experimentally tested in the literature, like for instance for database schemes, can efficiently handle graphs of only hundreds of vertices. We aim at devising general algorithmic techniques for drawing large graphs and at experimenting their usage in new visualization systems, thus contributing to devising the technology transfer from the algorithmic research on graph drawing to its application in networks visualiza- tion. As part of this goal, we developed analysis-enhancing layouts and created in a cooperation with CR8 Universit´e de Paris Sud a novel technique that preserves the readability of abstract visualizations while showing all elements. On a different level we also report the activity that has been done in collaboration with Universit´e de Paris Sud and C01 Istituto Nazionale Fisica per la Materia in Rome. In the first case through k-core decomposition we can significantly reduce the complexity of the graph, still preserving some information. In the latter case the reduction is made by selecting the vertices to preserve according to their betweennes value. 1 General Techniques for Visualizing Large Graphs In order to design a new tool that can handle larger networks, several issues have to be clarified first. Among many are the suitable representation of networks and precise descriptions for the connection between the analysis and the layouts. For this deliverable, the problem of finding a concise description is already tackled. 11 17 65 14 9 3 1 16 2 8 15 10 12 4 7 13 Figure 1: Reduced view of the Autonomous System graph. Nodes represent groups having similar connectivity and sizes are proportional to the number of elements in the group. Edges show the connections between groups and their thickness is proportional to the number of connections. Several visualization techniques have been established that support drawing large graphs, like the collapse of subgraphs or groups, masking irrelevant parts or multi-views. However, these procedures are useless as long as they are not...

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