This paper proposes novel methods for visualizing specifically the large power-law graphs that arise in sociology and the sciences. In such cases a large portion of edges can be shown to be less important and removed while preserving component connectedness and other features (e.g. cliques) to more clearly reveal the network’s underlying connection pathways. This simplification approach deterministically filters (instead of clustering) the graph to retain important node and edge semantics, and works both automatically and interactively. The improved graph filtering and layout is combined with a novel computer graphics anisotropic shading of the dense crisscrossing array of edges to yield a full social network and scale-free graph visualization system. Both quantitative analysis and visual results demonstrate the effectiveness of this approach.

Insight of multiscale networks could be accessed through the visualization of automatic multiscale clusterings. But results of these methods do not necessarily fulfill user expectations since they don’t provide error prone clusterings. In this article we propose a way to refine interactively these results by the use of multiscale grouping and ungrouping interactions. This approach revealed to give very good results on common networks, especially on Small World networks. Moreover, the linear algorithm makes that the method remains interactive on huge graphs with thousand of nodes.

Synchronous electrical activity in different brain regions is generally assumed to imply functional relationships between these regions. A measure for this synchrony is electroencephalography (EEG) coherence, computed between pairs of signals as a function of frequency. Existing high-density EEG coherence visualizations are generally either hypothesis-driven, or datadriven graph visualizations which are cluttered. In this paper, a new method is presented for data-driven visualization of highdensity EEG coherence, which strongly reduces clutter and is referred to as functional unit (FU) map. Starting from an initial graph, with vertices representing electrodes and edges representing significant coherences between electrode signals, we define an FU as a set of electrodes represented by a clique consisting of spatially connected vertices. In an FU map, the spatial relationship between electrodes is preserved, and all electrodes in one FU are assigned an identical gray value. Adjacent FUs are visualized with different gray values and FUs are connected by a line if the average coherence between FUs exceeds a threshold. Results obtained with our visualization are in accordance with known electrophysiological findings. FU maps can be used as a preprocessing step for conventional analysis.

A major challenge to achieving widespread use of software component technology in scientific computing is an effective migration strategy for existing, or legacy, source code. This paper describes initial work and challenges in automating the identification and generation of components using the ROSE compiler infrastructure and the Babel language interoperability tool. Babel enables calling interfaces expressed in the Scientific Interface Definition Language (SIDL) to be implemented in, and called from, an arbitrary combination of supported languages. ROSE is used to build specialized source-to-source translators that (1) extract a SIDL interface specification from information implicit in existing C++ source code and (2) transform Babel’s output to include dispatches to the legacy code. 1

Reuse between software systems is often not optimal. An important reason is that while at the functional level well-known modularization principles are applied for structuring functionality in modules, this is not the case at the build level for structuring files in directories. This leads to a situation where files are entangled in directory hierarchies and build processes, making it hard to extract functionality and to make functionality suitable for reuse. Consequently, software may not come available for reuse at all, or only in rather large chunks of functionality, which may lead to extra software dependencies.

I hereby declare that I am the sole author of this thesis. I authorize the University of Waterloo to lend this thesis to other institutions or individuals for the purpose of scholarly research.

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The software industry is increasingly confronted with the issues of understanding and maintaining a special type of software systems, namely distributed systems. Although these systems are usually implemented in an object-oriented fashion, they raise very specific, and technology-dependent, understandability and quality assessment challenges. This paper presents a visual approach for comprehending the design of distributed software systems, by using technology awareness to isolate the functionally-distinct units within the code, so that the blueprint of the system’s distributed behavior can be easily extracted. The approach provides means for observing the system’s distributed architecture, visualizing the structure of the functional entities, and understanding their collaboration within the system, while focusing the analysis to the most substantial code fragments that deserve being taken into consideration.

Abstract — A typical data-driven visualization of electroencephalography (EEG) coherence is a graph layout, with vertices representing electrodes and edges representing significant coherences between electrode signals. A drawback of this layout is its visual clutter for multichannel EEG. To reduce clutter, we define a functional unit (FU) as a data-driven region of interest (ROI). An FU is a spatially connected set of electrodes recording pairwise significantly coherent signals, represented in the coherence graph by a spatially connected clique. Earlier we presented two methods to detect FUs, a maximal clique based (MCB) method (time complexity O(3 n/3), with n the number of vertices) and a more efficient watershed based (WB) method (time complexity O(n 2 log n)). To reduce the potential over-segmentation of the WB method, we introduce an improved watershed based (IWB) method (time complexity O(n 2 log n)). The IWB method merges basins representing FUs during the segmentation if they are spatially connected and if their union is a clique. The WB and IWB method both are up to a factor of 100,000 faster than the MCB method for a typical multichannel setting with 128 EEG channels, thus making interactive visualization of multichannel EEG coherence possible. Results show that, considering the MCB method as the gold standard, the difference between IWB and MCB FU maps is smaller than between WB and MCB FU maps. We also introduce two novel group maps for data-driven group analysis as extensions of the IWB method. First, the group mean coherence map preserves dominant features from a collection of individual FU maps. Second, the group FU size map visualizes the average FU size per electrode across a collection of individual FU maps. Finally, we employ an extensive case study to evaluate the IWB FU map and the two new group maps for data-driven group analysis. Results, in accordance with conventional findings, indicate differences in EEG coherence between younger and older adults. However, they also suggest that an initial selection of hypothesis-driven ROIs could be extended with additional datadriven

The hierarchical edge bundle method clusters the graph edges to better understand and analyze graphs, but its effectiveness relies critically on the quality of the hierarchical organization of its nodes and edges. This paper proposes a novel graph visualization approach that extracts the community structure of a network and organizes it into a more balanced and meaningful hierarchy so that its edge bundle rendering better indicates its structure. Results on several data sets demonstrate that this approach clarifies realworld communication, collaboration and competition network structure and reveals information missed in previous visualizations.