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An Energy Model for Visual Graph Clustering
 Proceedings of the 11th International Symposium on Graph Drawing (GD 2003), LNCS 2912
, 2003
"... We introduce an energy model whose minimum energy drawings reveal the clusters of the drawn graph. Here a cluster is a set of nodes with many internal edges and few edges to nodes outside the set. The drawings of the bestknown force and energy models do not clearly show clusters for graphs whose ..."
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Cited by 41 (4 self)
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We introduce an energy model whose minimum energy drawings reveal the clusters of the drawn graph. Here a cluster is a set of nodes with many internal edges and few edges to nodes outside the set. The drawings of the bestknown force and energy models do not clearly show clusters for graphs whose diameter is small relative to the number of nodes. We formally characterize the minimum energy drawings of our energy model. This characterization shows in what sense the drawings separate clusters, and how the distance of separated clusters to the other nodes can be interpreted.
Visual Clustering of Graphs with Nonuniform Degrees
 Proceedings of the 13th International Symposium on Graph Drawing (GD 2005
, 2004
"... We discuss several criteria for clustering graphs, and identify two criteria which are not biased towards certain cluster sizes: the nodenormalized cut (also called cut ratio) and the edgenormalized cut. We present two energy models whose minimum energy drawings reveal clusters with respect to ..."
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Cited by 27 (2 self)
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We discuss several criteria for clustering graphs, and identify two criteria which are not biased towards certain cluster sizes: the nodenormalized cut (also called cut ratio) and the edgenormalized cut. We present two energy models whose minimum energy drawings reveal clusters with respect to these criteria.
Interactive visual clustering
 In Proceedings of the 10th International Conference on Intelligent User Interfaces. Intelligent User Interfaces
, 2007
"... Interactive Visual Clustering (IVC) is a novel method that allows a user to explore relational data sets interactively, in order to produce a clustering that satisfies their objectives. IVC combines springembedded graph layout techniques with user interaction and constrained clustering. This paper ..."
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Cited by 7 (1 self)
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Interactive Visual Clustering (IVC) is a novel method that allows a user to explore relational data sets interactively, in order to produce a clustering that satisfies their objectives. IVC combines springembedded graph layout techniques with user interaction and constrained clustering. This paper describes the IVC method, and gives experimental results on several synthetic and realworld data sets, showing that IVC yields better clustering performance than several alternative methods. ACM Classification: I2.6 [Artificial Intelligence]: Learning. H5.2 [Information interfaces and presentation]: Graphical
HGV: A Library for Hierarchies, Graphs, and Views
 American Chemical Society
, 2002
"... We introduce the base architecture of a software library which combines graphs, hierarchies, and views and describes the interactions between them. Each graph may have arbitrarily many hierarchies and each hierarchy may have arbitrarily many views. Both the hierarchies and the views can be added ..."
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Cited by 5 (3 self)
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We introduce the base architecture of a software library which combines graphs, hierarchies, and views and describes the interactions between them. Each graph may have arbitrarily many hierarchies and each hierarchy may have arbitrarily many views. Both the hierarchies and the views can be added and removed dynamically from the corresponding graph and hierarchy, respectively. The software library shall serve as a platform for algorithms and data structures on hierarchically structured graphs. Such graphs become increasingly important and occur in special applications, e. g., call graphs in software engineering or biochemical pathways, with a particular need to manipulate and draw graphs.
Visualisation of Social Networks using CAVALIER
"... Social Network Analysis is an approach to analysing organisations focusing on relationships as the most important aspect. In this paper we discuss visualisation techniques for Social Network Analysis, including springembedding and simulated annealing techniques. We introduce a visualisation techniq ..."
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Cited by 4 (2 self)
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Social Network Analysis is an approach to analysing organisations focusing on relationships as the most important aspect. In this paper we discuss visualisation techniques for Social Network Analysis, including springembedding and simulated annealing techniques. We introduce a visualisation technique based on Kohonen neural networks, and also introduce social flow diagrams for demonstrating the relationship between two forms of conceptual distance . Keywords: Social network analysis, Kohonen neural networks. 1 Social Network Analysis:
Maintaining Hierarchical Graph Views for Dynamic Graphs
, 2004
"... We describe a data structure for e#ciently maintaining views of dynamic graphs. ..."
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Cited by 3 (1 self)
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We describe a data structure for e#ciently maintaining views of dynamic graphs.
Multilevel Compound Tree – Construction Visualization and Interaction
 Proceedings of the International Conference on HumanComputer Interaction (INTERACT ’05), Lecture Notes in Computer Science 3583
, 2005
"... Abstract. Several hierarchical clustering techniques have been proposed to visualize large graphs, but fewer solutions suggest a focus based approach. We propose a multilevel clustering technique that produces in linear time a contextual clustered view depending on a userfocus. We get a tree of clu ..."
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Cited by 2 (0 self)
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Abstract. Several hierarchical clustering techniques have been proposed to visualize large graphs, but fewer solutions suggest a focus based approach. We propose a multilevel clustering technique that produces in linear time a contextual clustered view depending on a userfocus. We get a tree of clusters where each cluster called metasilhouette is itself hierarchically clustered into an inclusion tree of silhouettes. Resulting Multilevel Silhouette Tree (MuSiTree) has a specific structure called multilevel compound tree. This work builds upon previous work on a compound tree structure called MOTree. The work presented in this paper is a major improvement over previous work by (1) defining multilevel compound tree as a more generic structure, (2) proposing original spacefilling visualization techniques to display it, (3) defining relevant interaction model based on both focus changes and graph filtering techniques and (4) reporting from case studies in various fields: cocitation graphs, relateddocument graphs and social graphs. 1
Clustered Level Planarity
 Proc. 30th Int. Conf. Current Trends in Theory and Practice of Computer Science (SOFSEM’04
, 2004
"... Planarity is an important concept in graph drawing. It is generally accepted that planar drawings are well understandable. Recently, several variations of planarity have been studied for advanced graph concepts such as klevel graphs and clustered graphs. In klevel graphs, the vertices are partitio ..."
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Cited by 2 (1 self)
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Planarity is an important concept in graph drawing. It is generally accepted that planar drawings are well understandable. Recently, several variations of planarity have been studied for advanced graph concepts such as klevel graphs and clustered graphs. In klevel graphs, the vertices are partitioned into k levels and the vertices of one level are drawn on a horizontal line. In clustered graphs, there is a recursive clustering of the vertices according to a given nesting relation. In this paper we combine the concepts of level planarity and clustering and introduce clustered klevel graphs. For connected clustered level graphs we show that clustered klevel planarity can be tested in O(kV) time.
An Adaptable Data Structure for Dynamic Hierarchies on Graphs
"... We describe a fully dynamic data structure for e#ciently maintaining views of graphs. A view is generated from a base graph by the contraction of node subsets that are defined by an associated hierarchy. ..."
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We describe a fully dynamic data structure for e#ciently maintaining views of graphs. A view is generated from a base graph by the contraction of node subsets that are defined by an associated hierarchy.
Straightening Drawings of Clustered Hierarchical Graphs ⋆
"... Abstract. In this paper we deal with making drawings of clustered hierarchical graphs nicer. Given a planar graph G = (V, E) with an assignment of the vertices to horizontal layers, a plane drawing of G (with ymonotone edges) can be specified by stating for each layer the order of the vertices lyin ..."
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Abstract. In this paper we deal with making drawings of clustered hierarchical graphs nicer. Given a planar graph G = (V, E) with an assignment of the vertices to horizontal layers, a plane drawing of G (with ymonotone edges) can be specified by stating for each layer the order of the vertices lying on and the edges intersecting that layer. Given these orders and a recursive partition of the vertices into clusters, we want to draw G such that (i) edges are straightline segments, (ii) clusters lie in disjoint convex regions, (iii) no edge intersects a cluster boundary twice. First we investigate fast algorithms that produce drawings of the above type if the clustering fulfills certain conditions. We give two fast algorithms with different preconditions. Second we give a linear programming (LP) formulation that always yields a drawing that fulfills the above three requirements—if such a drawing exists. The size of our LP formulation is linear in the size of the graph. 1