The University of Florida Sparse Matrix Collection is a large, widely available, and actively growing set of sparse matrices that arise in real applications. Its matrices cover a wide spectrum of problem domains, both those arising from problems with underlying 2D or 3D geometry (structural engineering, computational fluid dynamics, model reduction, electromagnetics, semiconductor devices, thermodynamics, materials, acoustics, computer graphics/vision, robotics/kinematics, and other discretizations) and those that typically do not have such geometry (optimization, circuit simulation, networks and graphs, economic and financial modeling, theoretical and quantum chemistry, chemical process simulation, mathematics and statistics, and power networks). The collection meets a vital need that artificially-generated matrices cannot meet, and is widely used by the sparse matrix algorithms community for the development and performance evaluation of sparse matrix algorithms. The collection includes software for accessing and managing the collection, from MATLAB, Fortran, and C.

Large networks, having thousands of vertices and lines, can be found in many different areas, e. g: genealogies, flow graphs of programs, molecule, computer networks, transportation networks, social networks, intra/inter organisational networks ... Many standard network algorithms are very time and space consuming and therefore unsuitable for analysis of such networks. In the article we present some approaches to analysis and visualisation of large networks implemented in program Pajek. Some typical examples are also given. 1 Introduction Pajek (Slovene word for Spider) is a program, for Windows (32 bit), for analysis of large networks. It is freely available, for noncommercial use, at its homepage: http://vlado.fmf.uni-lj.si/pub/networks/pajek/ Large networks can be found in many different areas. Usually they are produced automatically, using computers, from different data sources that are already available in computer readable form. For example: large genealogies (genea...

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Embedding of a graph metric in Euclidean space efficiently and accurately is an important problem in general with applications in topology aggregation, closest mirror selection, and application level routing. We propose a new graph embedding scheme called Big-Bang Simulation (BBS), which simulates an explosion of particles under force field derived from embedding error. BBS is shown to be significantly more accurate, compared to all other embedding methods including GNP. We report an extensive simulation study of BBS compared with several known embedding schemes and show its advantage for distance estimation (as in the IDMaps project), mirror selection and topology aggregation.

Abstract. We present a randomized adaptive layout algorithm for nicely drawing undirected graphs that is based on the spring-embedder paradigm and contains several new heuristics to improve the convergence, including local temperatures, gravitational forces and the detection of rotations and oscillations. The proposed algorithm achieves drawings of high quality on a wide range of graphs with standard settings. Moreover, the algorithm is fast, being thus applicable on general undirected graphs of substantially larger size and complexity than before [9, 6, 3]. Aesthetically pleasing solutions are found in most cases. We give empirical data for the running time of the algorithm and the quality of the computed layouts. 1

Abstract—A compound graph is a frequently encountered type of data set. Relations are given between items, and a hierarchy is defined on the items as well. We present a new method for visualizing such compound graphs. Our approach is based on visually bundling the adjacency edges, i.e., non-hierarchical edges, together. We realize this as follows. We assume that the hierarchy is shown via a standard tree visualization method. Next, we bend each adjacency edge, modeled as a B-spline curve, toward the polyline defined by the path via the inclusion edges from one node to another. This hierarchical bundling reduces visual clutter and also visualizes implicit adjacency edges between parent nodes that are the result of explicit adjacency edges between their respective child nodes. Furthermore, hierarchical edge bundling is a generic method which can be used in conjunction with existing tree visualization techniques. We illustrate our technique by providing example visualizations and discuss the results based on an informal evaluation provided by potential users of such visualizations.

Many sensor network applications require that each node's sensor stream be annotated with its physical location in some common coordinate system. Manual measurement and configuration methods for obtaining location don't scale and are error-prone, and equipping sensors with GPS is often expensive and does not work in indoor and urban deployments. Sensor networks can therefore benefit from a self-configuring method where nodes cooperate with each other, estimate local distances to their neighbors, and converge to a consistent coordinate assignment. This paper describes a fully decentralized algorithm called AFL (Anchor-Free Localization) where nodes start from a random initial coordinate assignment and converge to a consistent solution using only local node interactions. The key idea in AFL is fold-freedom, where nodes first configure into a topology that resembles a scaled and unfolded version of the true configuration, and then run a force-based relaxation procedure.We show using extensive simulations under a variety of network sizes, node densities, and distance estimation errors that our algorithm is superior to previously proposed methods that incrementally compute the coordinates of nodes in the network, in terms of its ability to compute correct coordinates under a wider variety of conditions and its robustness to measurement errors.

Abstract ⎯ This paper describes the development and application of visualisation techniques for users to access and explore information in a digital library effectively and intuitively. Salient semantic structures and citation patterns are extracted from several collections of documents, including the ACM SIGCHI conference proceedings (1995 ⎯ 1997) and ACM Hypertext conference proceedings (1987 ⎯ 1998), using Latent Semantic Indexing and Pathfinder Network Scaling. The unique spatial metaphor leads to a natural combination of search and navigation within the same semantic space in a 3-dimensional virtual world. Author co-citation patterns are visualised through a number of author co-citation maps in attempts to reveal the structure of the field of hypertext, including an overall co-citation map of 367 authors and three periodical maps. These maps highlight predominant research areas in the field. This approach provides a means of transcending the boundaries of collections of documents and visualising more profound patterns in terms of semantic structures and co-citation networks. © 1999 Elsevier Science Ltd. All rights reserved. 1.

We describe a heuristic method for drawing graphs which uses a multilevel framework combined with a force-directed placement algorithm.

We describe Gevol, a system that visualizes the evolution of software using a novel graph drawing technique for visualization of large graphs with a temporal component. Gevol extracts information about a Java program stored within a CVS version control system and displays it using a temporal graph visualizer. This information can be used by programmers to understand the evolution of a legacy program: Why is the program structured the way it is? Which programmers were responsible for which parts of the program during which time periods? Which parts of the program appear unstable over long periods of time and may need to be rewritten? This type of information will complement that produced by more static tools such as source code browsers, slicers, and static analyzers. 1