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15
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 58 (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 34 (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.
In situ exploration of large dynamic networks
 IEEE TVCG
"... Abstract—The analysis of large dynamic networks poses a challenge in many fields, ranging from large botnets to social networks. As dynamic networks exhibit different characteristics, e.g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization ..."
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Cited by 16 (2 self)
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Abstract—The analysis of large dynamic networks poses a challenge in many fields, ranging from large botnets to social networks. As dynamic networks exhibit different characteristics, e.g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization – a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown – right in the place where they are positioned, thus supporting the user’s overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks. Index Terms—Dynamic graph data, multiform visualization, multifocus+context. 1
Interactive visual clustering
 In Proceedings of the Twelfth International Conference on 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 satises their objectives. IVC combines springembedded graph layout with user interaction and constrained clustering. Experimental results ..."
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Cited by 13 (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 satises their objectives. IVC combines springembedded graph layout with user interaction and constrained clustering. Experimental results on several synthetic and realworld data sets show that IVC yields better clustering performance than alternative methods. ACM Classication: I2.6 [Articial Intelligence]: Learning. H5.2 [Information interfaces and presentation]: Graphical user interfaces.
How to Draw Clustered Weight Graphs Using a Multilevel ForceDirected Graph Drawing Algorithm
 11th International Conference on Information Visualization (IV07), 757764
, 2007
"... Visualization of clustered graphs has been a research area since many years. In this paper, we describe a new approach that can be used in real application where graph does not contain only topological information but also extrinsic parameters (i.e. user attributes on edges and nodes). In the case ..."
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Cited by 6 (0 self)
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Visualization of clustered graphs has been a research area since many years. In this paper, we describe a new approach that can be used in real application where graph does not contain only topological information but also extrinsic parameters (i.e. user attributes on edges and nodes). In the case of forcedirected algorithm, management of attributes corresponds to take into account edge weights. We propose an extension of the GRIP algorithm in order to manage edge weights. Furthermore, by using Voronoi diagram we constrained that algorithm to draw each cluster in a non overlapping convex region. Using these two extensions we obtained an algorithm that draw clustered weighted graphs. Experimentation has been done on data coming from biology where the network is the genesproteins interaction graph and where the attributes are gene expression values from microarray experiments.
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.
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 4 (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
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:
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 4 (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.
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.