GMap: Visualizing graphs and clusters as maps

Gansner, Emden R.; Hu, Yifan; Kobourov, Stephen G.

doi:10.1109/pacificvis.2010.5429590

Cited by 111 publications

(74 citation statements)

References 32 publications

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“…Ganser et al [10] have proposed GMap, a system for drawing graphs as maps. Given a node partition, they show how to draw a graph as a political map using a Voronoi diagram based shape.…”

Section: Related Workmentioning

confidence: 99%

Drawing Clustered Graphs as Topographic Maps

Gronemann

Jünger

2013

Graph Drawing

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Abstract. The visualization of clustered graphs is an essential tool for the analysis of networks, in particular, social networks, in which clustering techniques like community detection can reveal various structural properties. In this paper, we show how clustered graphs can be drawn as topographic maps, a type of map easily understandable by users not familiar with information visualization. Elevation levels of connected entities correspond to the nested structure of the cluster hierarchy. We present methods for initial node placement and describe a tree mapping based algorithm that produces an area efficient layout. Given this layout, a triangular irregular mesh is generated that is used to extract the elevation data for rendering the map. In addition, the mesh enables the routing of edges based on the topographic features of the map.

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“…Ganser et al [10] have proposed GMap, a system for drawing graphs as maps. Given a node partition, they show how to draw a graph as a political map using a Voronoi diagram based shape.…”

Section: Related Workmentioning

confidence: 99%

Drawing Clustered Graphs as Topographic Maps

Gronemann

Jünger

2013

Graph Drawing

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“…Gansner et al have suggested the use of geographic maps for enhancing clusters and visualizing similarities (Gansner et al, 2009;Gansner et al, 2010). They first compute a partition of the nodes by applying a cluster algorithm, then use a force-directed method or MDS to compute a layout, and finally use both to create a colored map.…”

Section: Related Workmentioning

confidence: 99%

MolMap - Visualizing Molecule Libraries as Topographic Maps

Gronemann

Jünger

Kriege

et al. 2013

Proceedings of the International Conference on Computer Graphics Theory and Applications and International Conference on Inform

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Abstract:We present a new application for graph drawing and visualization in the context of drug discovery. Combining the scaffold-based cluster hierarchy with molecular similarity graphs -both standard concepts in cheminformatics -allows one to get new insights for analyzing large molecule libraries. The derived clustered graphs represent different aspects of structural similarity. We suggest visualizing them as topographic maps. Since the cluster hierarchy does not reflect the underlying graph structure as in (Gronemann and Jünger, 2012), we suggest a new partitioning algorithm that takes the edges of the graph into account. Experiments show that the new algorithm leads to significant improvements in terms of the edge lengths in the obtained drawings.

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“…The problem of maximum differential coloring of graphs arises in the context of coloring a map in which not all regions are necessarily contiguous [7,6]. A variation of the differential graph coloring problem was studied by Dillencourt et al [4], under the assumption that all colors in the color spectrum are available.…”

Section: Related Workmentioning

confidence: 99%

“…Gansner et al [6] proposed a heuristic based on a relaxation of the discrete MaxMin problem (1) into a continuous maximization problem of 2-norm:…”

Section: Algorithms For Maximum Differential Coloringmentioning

confidence: 99%

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On Maximum Differential Graph Coloring

Kobourov

Veeramoni

2011

Graph Drawing

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Abstract. We study the maximum differential graph coloring problem, in which the goal is to find a vertex labeling for a given undirected graph that maximizes the label difference along the edges. This problem has its origin in map coloring, where not all countries are necessarily contiguous. We define the differential chromatic number and establish the equivalence of the maximum differential coloring problem to that of k-Hamiltonian path. As computing the maximum differential coloring is NP-Complete, we describe an exact backtracking algorithm and a spectral-based heuristic. We also discuss lower bounds and upper bounds for the differential chromatic number for several classes of graphs.

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GMap: Visualizing graphs and clusters as maps

Cited by 111 publications

References 32 publications

Drawing Clustered Graphs as Topographic Maps

Drawing Clustered Graphs as Topographic Maps

MolMap - Visualizing Molecule Libraries as Topographic Maps

On Maximum Differential Graph Coloring

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