Visualization of Clustered Directed Acyclic Graphs without Node Overlapping

Kumar, Pushpa; Zhang, Kang; Wang, Yuke

doi:10.1109/iv.2008.85

Cited by 6 publications

(12 citation statements)

References 12 publications

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“…E-Spring algorithm [9] was proposed to eliminate node overlaps in clustered DAGs. The attractive forces on the springs, and the repulsive forces between the positive charges act together to generate a drawing free of node overlaps, when the system reaches a state of equilibrium.…”

Section: Idea Of Forcesmentioning

confidence: 99%

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Visualization of clustered directed acyclic graphs with node interleaving

Kumar

Zhang

2009

Proceedings of the 2009 ACM Symposium on Applied Computing

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Graph drawing and visualization represent structural information as diagrams of abstract graphs and networks. An important subset of graphs is directed acyclic graphs (DAGs). E-Spring algorithm, extended from the popular spring embedder model, eliminates node overlaps in clustered DAGs by modeling nodes as charged particles whose repulsion is controlled by edges modeled as springs. The drawing process needs to reach a stable state when the average distances of separation between nodes are near optimal. This paper presents an enhancement to E-Spring to introduce a stopping condition, which reduces equilibrium distances between nodes and therefore results in a significantly reduced area for DAG visualization. It imposes an upper bound on the repulsive forces between nodes based on graph geometry. The algorithm employs node interleaving to eliminate any residual node overlaps. These new techniques have been validated by visualizing eBay buyer-seller relationships and resulted in overall area reductions in the range of 45% to 79%.

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Section: Idea Of Forcesmentioning

confidence: 99%

“…E-Spring algorithm [9] uses animation for drawing clustered DAGs without node overlap to preserve the user's mental map [25]. The animation needs to reach a stable state when the average distances of separation between nodes are near optimal.…”

Section: Introductionmentioning

confidence: 99%

Visualization of clustered directed acyclic graphs with node interleaving

Kumar

Zhang

2009

Proceedings of the 2009 ACM Symposium on Applied Computing

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“…E-Spring algorithm [20] was proposed to eliminate node overlaps in the drawing D(Gc) of clustered directed acyclic graphs (Gc), especially for labeled nodes with finite length string labels. In this algorithm, nodes are modeled as unequally charged particles, with springs representing edges of the graph.…”

Section: E-spring Algorithmmentioning

confidence: 99%

“…E-Spring algorithm [20][21] [22] works well with graphs that follow a clustered tree structure, where a parent node has many child nodes in its vicinity to form clusters, and nodes have finite length string labels.…”

Section: Tree Extractionmentioning

confidence: 99%

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From Tree to Graph - Experiments with E-Spring Algorithm

Kumar¹,

Zhang²,

Huang

2009

Visual Information Communication

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Abstract. Graph drawing and visualization represent structural information as diagrams of abstract graphs and networks. E-Spring Algorithm, derived from the popular spring embedder model, was proposed to eliminate node overlaps in the drawings of clustered directed acyclic graphs Gc. In this paper, we apply the E-Spring algorithm to general graphs by minimizing edge-node intersections. Initially, a tree structure is extracted from the original graph using the breadth-first search (BFS) algorithm. The extracted tree is then visualized without node overlaps using the E-Spring algorithm, and the remaining non-tree edges are appended to this visualization. A post-processing step that implements edge routing is performed on the obtained visualization to eliminate residual edge-node intersections. This method has been validated by visualizing eBay buyer-seller relationships and Graph Catalog benchmarking data.

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Node overlap removal in clustered directed acyclic graphs

Kumar¹,

Zhang²

2009

Journal of Visual Languages & Computing

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Visualization of Clustered Directed Acyclic Graphs without Node Overlapping

Cited by 6 publications

References 12 publications

Visualization of clustered directed acyclic graphs with node interleaving

Visualization of clustered directed acyclic graphs with node interleaving

From Tree to Graph - Experiments with E-Spring Algorithm

Node overlap removal in clustered directed acyclic graphs

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