Tulip — A Huge Graph Visualization Framework

Auber, David

doi:10.1007/978-3-642-18638-7_5

Cited by 228 publications

(210 citation statements)

References 15 publications

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“…Animated links have also been added to the Tulip graph visualization library [1]. The dash patterns used by Tulip move along a straight line from start to end and consist of repeating "greater than" symbols (" ") of alternating colors.…”

Section: Animated Directed-edge Representationsmentioning

confidence: 99%

An extended evaluation of the readability of tapered, animated, and textured directed-edge representations in node-link graphs

Holten

Isenberg

Wijk

et al. 2011

2011 IEEE Pacific Visualization Symposium

102

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Section: Animated Directed-edge Representationsmentioning

confidence: 99%

An extended evaluation of the readability of tapered, animated, and textured directed-edge representations in node-link graphs

Holten

Isenberg

Wijk

et al. 2011

2011 IEEE Pacific Visualization Symposium

102

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“…We also used the Louvain algorithm [4] that actually produces a hierarchical clustering. We directly used the source code provided by the respective authors, then ran the algorithm and visualized the results using the Graph Visualization framework Tulip [2]. All of these procedure are unsupervised and do not require any parametrization.…”

Section: College Football Networkmentioning

confidence: 99%

Assessing the quality of multilevel graph clustering

Queyroi

Delest

Fédou

et al. 2013

Data Min Knowl Disc

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Hierarchical clustering of graphs is a useful strategy to mine, explore and visualize graphs. Popular approaches define ad hoc procedures to decide how subgraphs are subdivided or nested. The popularity of graph hierarchies certainly relates to the relevance of multilevel models appearing in the natural and social sciences. For instance, current models in biology (genomics and/or proteomics) try to capture the multilevel nature of networks formed by various biological entities; cities and worldwide city systems in geography can also be described as multilevel networks. In our opinion, a theory supporting these multilevel clustering approaches is yet to be developed. Indeed, to the best of our knowledge there are no known optimization multilevel criteria guiding the construction of a hierarchy of clusters: the hierarchy basically is an artefact of an iterative procedure. The main results of this paper contribute to such a multilevel clustering theory, by designing and studying a multilevel modularity measure for hierarchically clustered graphs, explicitly taking the nesting structure of clusters into account. The multilevel modularity we propose generalizes a modularity measure introduced by Mancoridis et al. in the context of reverse software engineering. The measure we designed recursively traverses the hierarchy of clusters and computes a onevariable polynomial encoding the intra and inter-cluster densities appearing at all levels in a hierarchical clustering. The resulting polynomial reflects how the graph combines with the hierarchy of clusters and can be used to assess the quality of a hierarchical clustering. We discuss archetypal examples as proof-of-concept. We also look at how this multilevel modularity acts on a popular real world example.

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“…The implementation uses the small-step operational semantics given above. Some of these semantic rules require a copy of the graph program; this is done efficiently in PORGY thanks to the cloning functionalities of the underlying TULIP system [5].…”

Section: Related Work and Conclusionmentioning

confidence: 99%

A Strategy Language for Graph Rewriting

Fernández¹,

Kirchner²,

Namet³

2012

Lecture Notes in Computer Science

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Abstract. We give a formal semantics for a graph-based programming language, where a program consists of a collection of graph rewriting rules, a user-defined strategy to control the application of rules, and an initial graph to be rewritten. The traditional operators found in strategy languages for term rewriting have been adapted to deal with the more general setting of graph rewriting, and some new constructs have been included in the language to deal with graph traversal and management of rewriting positions in the graph. This language is part of the graph transformation and visualisation environment PORGY.

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Tulip — A Huge Graph Visualization Framework

Cited by 228 publications

References 15 publications

An extended evaluation of the readability of tapered, animated, and textured directed-edge representations in node-link graphs

An extended evaluation of the readability of tapered, animated, and textured directed-edge representations in node-link graphs

Assessing the quality of multilevel graph clustering

A Strategy Language for Graph Rewriting

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