123,874 research outputs found
A nested alignment graph kernel through the dynamic time warping framework
In this paper, we propose a novel nested alignment graph kernel drawing on depth-based complexity traces and the dynamic time warping framework. Specifically, for a pair of graphs, we commence by computing the depth-based complexity traces rooted at the centroid vertices. The resulting kernel for the graphs is defined by measuring the global alignment kernel, which is developed through the dynamic time warping framework, between the complexity traces. We show that the proposed kernel simultaneously considers the local and global graph characteristics in terms of the complexity traces, but also provides richer statistic measures by incorporating the whole spectrum of alignment costs between these traces. Our experiments demonstrate the effectiveness and efficiency of the proposed kernel
Coherent Time-Varying Graph Drawing with Multifocus+Context Interaction
Abstract—We present a new approach for time-varying graph drawing that achieves both spatiotemporal coherence and multifocus+context visualization in a single framework. Our approach utilizes existing graph layout algorithms to produce the initial graph layout, and formulates the problem of generating coherent time-varying graph visualization with the focus+context capability as a specially-tailored deformation optimization problem. We adopt the concept of the super graph to maintain spatiotemporal coherence and further balance the needs for aesthetic quality and dynamic stability when interacting with time-varying graphs through focus+context visualization. Our method is particularly useful for multifocus+context visualization of time-varying graphs where we can preserve the mental map by preventing nodes in the focus from undergoing abrupt changes in size and location in the time sequence. Experiments demonstrate that our method strikes a good balance between maintaining spatiotemporal coherence and accentuating visual foci, thus providing a more engaging viewing experience for the users. Index Terms—Graph drawing, time-varying graphs, spatiotemporal coherence, focus+context visualization
A Regularized Graph Layout Framework for Dynamic Network Visualization
Many real-world networks, including social and information networks, are
dynamic structures that evolve over time. Such dynamic networks are typically
visualized using a sequence of static graph layouts. In addition to providing a
visual representation of the network structure at each time step, the sequence
should preserve the mental map between layouts of consecutive time steps to
allow a human to interpret the temporal evolution of the network. In this
paper, we propose a framework for dynamic network visualization in the on-line
setting where only present and past graph snapshots are available to create the
present layout. The proposed framework creates regularized graph layouts by
augmenting the cost function of a static graph layout algorithm with a grouping
penalty, which discourages nodes from deviating too far from other nodes
belonging to the same group, and a temporal penalty, which discourages large
node movements between consecutive time steps. The penalties increase the
stability of the layout sequence, thus preserving the mental map. We introduce
two dynamic layout algorithms within the proposed framework, namely dynamic
multidimensional scaling (DMDS) and dynamic graph Laplacian layout (DGLL). We
apply these algorithms on several data sets to illustrate the importance of
both grouping and temporal regularization for producing interpretable
visualizations of dynamic networks.Comment: To appear in Data Mining and Knowledge Discovery, supporting material
(animations and MATLAB toolbox) available at
http://tbayes.eecs.umich.edu/xukevin/visualization_dmkd_201
A comparative evaluation of dynamic visualisation tools
Despite their potential applications in software comprehension, it appears that dynamic visualisation tools are seldom used outside the research laboratory. This paper presents an empirical evaluation of five dynamic visualisation tools - AVID, Jinsight, jRMTool, Together ControlCenter diagrams and Together ControlCenter debugger. The tools were evaluated on a number of general software comprehension and specific reverse engineering tasks using the HotDraw objectoriented framework. The tasks considered typical comprehension issues, including identification of software structure and behaviour, design pattern extraction, extensibility potential, maintenance issues, functionality location, and runtime load. The results revealed that the level of abstraction employed by a tool affects its success in different tasks, and that tools were more successful in addressing specific reverse engineering tasks than general software comprehension activities. It was found that no one tool performs well in all tasks, and some tasks were beyond the capabilities of all five tools. This paper concludes with suggestions for improving the efficacy of such tools
Fast filtering and animation of large dynamic networks
Detecting and visualizing what are the most relevant changes in an evolving
network is an open challenge in several domains. We present a fast algorithm
that filters subsets of the strongest nodes and edges representing an evolving
weighted graph and visualize it by either creating a movie, or by streaming it
to an interactive network visualization tool. The algorithm is an approximation
of exponential sliding time-window that scales linearly with the number of
interactions. We compare the algorithm against rectangular and exponential
sliding time-window methods. Our network filtering algorithm: i) captures
persistent trends in the structure of dynamic weighted networks, ii) smoothens
transitions between the snapshots of dynamic network, and iii) uses limited
memory and processor time. The algorithm is publicly available as open-source
software.Comment: 6 figures, 2 table
GiViP: A Visual Profiler for Distributed Graph Processing Systems
Analyzing large-scale graphs provides valuable insights in different
application scenarios. While many graph processing systems working on top of
distributed infrastructures have been proposed to deal with big graphs, the
tasks of profiling and debugging their massive computations remain time
consuming and error-prone. This paper presents GiViP, a visual profiler for
distributed graph processing systems based on a Pregel-like computation model.
GiViP captures the huge amount of messages exchanged throughout a computation
and provides an interactive user interface for the visual analysis of the
collected data. We show how to take advantage of GiViP to detect anomalies
related to the computation and to the infrastructure, such as slow computing
units and anomalous message patterns.Comment: Appears in the Proceedings of the 25th International Symposium on
Graph Drawing and Network Visualization (GD 2017
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