23,332 research outputs found
Animating the development of Social Networks over time using a dynamic extension of multidimensional scaling
The animation of network visualizations poses technical and theoretical
challenges. Rather stable patterns are required before the mental map enables a
user to make inferences over time. In order to enhance stability, we developed
an extension of stress-minimization with developments over time. This dynamic
layouter is no longer based on linear interpolation between independent static
visualizations, but change over time is used as a parameter in the
optimization. Because of our focus on structural change versus stability the
attention is shifted from the relational graph to the latent eigenvectors of
matrices. The approach is illustrated with animations for the journal citation
environments of Social Networks, the (co-)author networks in the carrying
community of this journal, and the topical development using relations among
its title words. Our results are also compared with animations based on
PajekToSVGAnim and SoNIA
The Galois Complexity of Graph Drawing: Why Numerical Solutions are Ubiquitous for Force-Directed, Spectral, and Circle Packing Drawings
Many well-known graph drawing techniques, including force directed drawings,
spectral graph layouts, multidimensional scaling, and circle packings, have
algebraic formulations. However, practical methods for producing such drawings
ubiquitously use iterative numerical approximations rather than constructing
and then solving algebraic expressions representing their exact solutions. To
explain this phenomenon, we use Galois theory to show that many variants of
these problems have solutions that cannot be expressed by nested radicals or
nested roots of low-degree polynomials. Hence, such solutions cannot be
computed exactly even in extended computational models that include such
operations.Comment: Graph Drawing 201
Embedding Spatial Software Visualization in the IDE: an Exploratory Study
Software visualization can be of great use for understanding and exploring a
software system in an intuitive manner. Spatial representation of software is a
promising approach of increasing interest. However, little is known about how
developers interact with spatial visualizations that are embedded in the IDE.
In this paper, we present a pilot study that explores the use of Software
Cartography for program comprehension of an unknown system. We investigated
whether developers establish a spatial memory of the system, whether clustering
by topic offers a sound base layout, and how developers interact with maps. We
report our results in the form of observations, hypotheses, and implications.
Key findings are a) that developers made good use of the map to inspect search
results and call graphs, and b) that developers found the base layout
surprising and often confusing. We conclude with concrete advice for the design
of embedded software maps.Comment: To appear in proceedings of SOFTVIS 2010 conferenc
Visualisation techniques for users and designers of layout algorithms
Visualisation systems consisting of a set of components through which data and interaction commands flow have been explored by a number of researchers. Such hybrid and multistage algorithms can be used to reduce overall computation time, and to provide views of the data that show intermediate results and the outputs of complementary algorithms. In this paper we present work on expanding the range and variety of such components, with two new techniques for analysing and controlling the performance of visualisation processes. While the techniques presented are quite different, they are unified within HIVE: a visualisation system based upon a data-flow model and visual programming. Embodied within this system is a framework for weaving together our visualisation components to better afford insight into data and also deepen understanding of the process of the data's visualisation. We describe the new components and offer short case studies of their application. We demonstrate that both analysts and visualisation designers can benefit from a rich set of components and integrated tools for profiling performance
Construction of embedded fMRI resting state functional connectivity networks using manifold learning
We construct embedded functional connectivity networks (FCN) from benchmark
resting-state functional magnetic resonance imaging (rsfMRI) data acquired from
patients with schizophrenia and healthy controls based on linear and nonlinear
manifold learning algorithms, namely, Multidimensional Scaling (MDS), Isometric
Feature Mapping (ISOMAP) and Diffusion Maps. Furthermore, based on key global
graph-theoretical properties of the embedded FCN, we compare their
classification potential using machine learning techniques. We also assess the
performance of two metrics that are widely used for the construction of FCN
from fMRI, namely the Euclidean distance and the lagged cross-correlation
metric. We show that the FCN constructed with Diffusion Maps and the lagged
cross-correlation metric outperform the other combinations
- …