Tugging Graphs Faster: Efficiently Modifying Path-Preserving Hierarchies for Browsing Paths

Archambault, Daniel; Munzner, Tamara

doi:10.1109/tvcg.2010.60

Cited by 22 publications

(23 citation statements)

References 26 publications

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“…The approach, however, does not represent cellular components. Other approaches that group nodes in two-dimensional space have been proposed, such as constrained force-directed layout [13], constrained projections [14], hierarchical graph placement [15,16,17] and others [18,19,20,21]. Despite their good performance even for large networks, the cell structure is not taken into consideration in either of those cases.…”

Section: Introductionmentioning

confidence: 99%

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components

Heberle

Carazzolle

Telles

et al. 2017

Preprint

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Background: The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. proteins, metabolites etc.) are represented by nodes and their interactions are represented by edges. An appropriate visualization of data is crucial for understanding such networks, since pathways are related to functions that occur in specific regions of the cell. The force-directed layout is an important and widely used technique to draw networks according to their topologies. Placing the networks into cellular compartments helps to quickly identify where network elements are located and, more specifically, concentrated. Currently, only a few tools provide the capability of visually organizing networks by cellular compartments. Most of them cannot handle large and dense networks. Even for small networks with hundreds of nodes the available tools are not able to reposition the network while the user is interacting, limiting the visual exploration capability. Results: Here we propose CellNetVis, a web tool to easily display biological networks in a cell diagram employing a constrained force-directed layout algorithm. The tool is freely available and open-source. It was originally designed for networks generated by the Integrated Interactome System and can be used with networks from others databases, like InnateDB. Conclusions: CellNetVis has demonstrated to be applicable for dynamic investigation of complex networks over a consistent representation of a cell on the Web, with capabilities not matched elsewhere.

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Section: Introductionmentioning

confidence: 99%

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components

Heberle

Carazzolle

Telles

et al. 2017

Preprint

View full text Add to dashboard Cite

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“…Muelder and Ma [44] adopted treemaps to hierarchically organize graphs, so that they can be efficiently mapped onto 2D layouts. GrouseFlocks and TugGraph [4,5,6] support interactions to add and remove aggregated nodes on demand, which provides users with more flexibility to organize node clusters. Vehlow et al [48] represented communities as abstract nodes and highlighted nodes between communities through partially aggregated graphs.…”

Section: Related Work 21 Node-link Diagramsmentioning

confidence: 99%

Evaluation of Graph Sampling: A Visualization Perspective

Cao

Archambault

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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Graph sampling is frequently used to address scalability issues when analyzing large graphs. Many algorithms have been proposed to sample graphs, and the performance of these algorithms has been quantified through metrics based on graph structural properties preserved by the sampling: degree distribution, clustering coefficient, and others. However, a perspective that is missing is the impact of these sampling strategies on the resultant visualizations. In this paper, we present the results of three user studies that investigate how sampling strategies influence node-link visualizations of graphs. In particular, five sampling strategies widely used in the graph mining literature are tested to determine how well they preserve visual features in node-link diagrams. Our results show that depending on the sampling strategy used different visual features are preserved. These results provide a complimentary view to metric evaluations conducted in the graph mining literature and provide an impetus to conduct future visualization studies.

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“…An approach to visualize the evolution of groups for dynamic graphs is to draw groups as rectangles on top of a flow‐like group evolution visualization; between‐group edges are aggregated and the subgraphs of individual groups are drawn within the group representations [VBAW15] . Disjoint hierarchical groups can be visualized using nested rectangular [ASH14, DM12, DM14a, RPD09] or circular [AMA07a, AMA08, AMA09, AMA11] (3D: spherical [vHvW04]) group structure representations (Figure a.2). Reitz et al .…”

Section: Taxonomy Of Vertex Group Structure Visualizationsmentioning

confidence: 99%

Visualizing Group Structures in Graphs: A Survey

Vehlow

Beck

Weiskopf

2016

Computer Graphics Forum

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Graph visualizations encode relationships between objects. Abstracting the objects into group structures provides an overview of the data. Groups can be disjoint or overlapping, and might be organized hierarchically. However, the underlying graph still needs to be represented for analyzing the data in more depth. This work surveys research in visualizing group structures as part of graph diagrams. A particular focus is the explicit visual encoding of groups, rather than only using graph layout to indicate groups implicitly. We introduce a taxonomy of visualization techniques structuring the field into four main categories: visual node attributes vary properties of the node representation to encode the grouping, juxtaposed approaches use two separate visualizations, superimposed techniques work with two aligned visual layers, and embedded visualizations tightly integrate group and graph representation. The derived taxonomies for group structure and visualization types are also applied to group visualizations of edges. We survey group-only, group-node, group-edge and group-network tasks that are described in the literature as use cases of group visualizations. We discuss results from evaluations of existing visualization techniques as well as main areas of application. Finally, we report future challenges based on interviews we conducted with leading researchers of the field.

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Tugging Graphs Faster: Efficiently Modifying Path-Preserving Hierarchies for Browsing Paths

Cited by 22 publications

References 26 publications

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components

CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components

Evaluation of Graph Sampling: A Visualization Perspective

Visualizing Group Structures in Graphs: A Survey

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