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1. Unifying the framework of Multi-Layer Network and Visual Analytics

Author

Søren Knudsen, Jan Aerts, Daniel Archambault, Remco Chang, Jean-Daniel Fekete, Martin Grandjean, Jessie Kennedy, Mikko Kivelä, Matteo Magnani, Helen Purchase, Christian Tominski, Paola Valdivia, Tatiana von Landesberger, University of Calgary, University of Copenhagen = Københavns Universitet (KU), Hasselt University (UHasselt), Department of Computer Science [Swansea], Swansea University, Tufts University [Medford], Analysis and Visualization (AVIZ), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Lausanne (UNIL), Edinburgh Napier University, Aalto University School of Science and Technology [Aalto, Finland], Uppsala University, Department of Computing Science, University of Glasgow, Computer Science and Electrical Engineering, University of Rostock, Department of Computer Science, Technische Universität Darmstadt (TU Darmstadt), University of Copenhagen = Københavns Universitet (UCPH), Université de Lausanne = University of Lausanne (UNIL), and Technische Universität Darmstadt - Technical University of Darmstadt (TU Darmstadt)

Subjects
[INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC], [SHS.STAT]Humanities and Social Sciences/Methods and statistics, Information design, Data visualization, Social network analyses, graph theory, [SHS.INFO]Humanities and Social Sciences/Library and information sciences, Complex networks, Multi-layer networks, Complexity, Networks, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], Data visualisation
Abstract

International audience; The notion of multi-layer networks introduces a general framework and common vocabulary for existing ideas in complex network theory. In doing so, it is possible to understand and compare these dierent ideas in a new and more fruitful manner. However, to make this operationalizable to the visualization and visual analytics community, we need more clarity. For example: What is a layer? What are the semantics of interlayer edges, and specifically, identity links between layers? Can dierent multilayered networks be expressed or implemented in the same way? And vice versa, can one multilayered network be expressed or implemented in dierent ways?

Published
2019

2. Tulip III

Author

David Auber, Daniel Archambault, Romain Bourqui, Maylis Delest, Jonathan Dubois, Bruno Pinaud, Antoine Lambert, Patrick Mary, Morgan Mathiaut, Guy Melancon, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Department of Computer Science [Swansea], and Swansea University

Subjects
0303 health sciences, 03 medical and health sciences, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, 020207 software engineering, 02 engineering and technology, 030304 developmental biology
Abstract

International audience; Although this article presents a system and discusses its design, its content goes much further. In a sense, this paper is a position paper following 10 years of lessons learned working in graph visualization, developing new visualization techniques, and building systems for users. The strategy we have adopted is to develop, maintain, and improve the Tulip framework, aiming for an architecture with optimal data structure management from which target applications can be easily derived.

Published
2014
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3. The Tulip 3 Framework: A Scalable Software Library for Information Visualization Applications Based on Relational Data

Author

David Auber, Daniel Archambault, Romain Bourqui, Antoine Lambert, Morgan Mathiaut, Patrick Mary, Maylis Delest, Jonathan Dubois, Guy Melançon, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Graph Visualization and Interactive Exploration (GRAVITE), Université Sciences et Technologies - Bordeaux 1-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Clique Strategic Research Cluster (Clique), University College Dublin [Dublin] (UCD), INRIA, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), and Université Sciences et Technologies - Bordeaux 1 (UB)-Inria Bordeaux - Sud-Ouest

Subjects
graph drawing, Graph visualization framework, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], information visualization, interactive graph visualization, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], graph hierarchy interactive visualization, scalable graph visualization
Abstract

Tulip is an information visualization framework dedicated to the analysis and visualization of relational data. Based on a decade of research and development of this framework, we present the architecture, consisting of a suite of tools and techniques, that can be used to address a large variety of domain-specific problems. With Tulip, we aim to provide the developer with a complete library, supporting the design of interactive information visualization applications for relational data that can be tailored to the problems he or she is addressing. The current framework enables the development of algorithms, visual encodings, interaction techniques, data models, and domain-specific visualizations. The software model facilitates the reuse of components and allows the developers to focus on programming their application. This development pipeline makes the framework efficient for research prototyping as well as the development of end-user applications.

Published
2012

4. ImPrEd: An Improved Force-Directed Algorithm that Prevents Nodes from Crossing Edges

Author

Romain Bourqui, Paolo Simonetto, Daniel Archambault, David Auber, Simonetto, Paolo, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Graph Visualization and Interactive Exploration (GRAVITE), Université Sciences et Technologies - Bordeaux 1 (UB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Clique Strategic Research Cluster (Clique), University College Dublin [Dublin] (UCD), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), and Université Sciences et Technologies - Bordeaux 1-Inria Bordeaux - Sud-Ouest

Subjects
Graph labeling, Computer science, Graph embedding, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Mixed graph, 02 engineering and technology, Strength of a graph, ACM: G.: Mathematics of Computing/G.2: DISCRETE MATHEMATICS/G.2.2: Graph Theory/G.2.2.0: Graph algorithms, Semi-symmetric graph, law.invention, symbols.namesake, law, Line graph, 0202 electrical engineering, electronic engineering, information engineering, Complement graph, Multigraph, Graph Layout, Voltage graph, Quartic graph, 020207 software engineering, Graph theory, Directed graph, Topological graph, Computer Graphics and Computer-Aided Design, Feedback arc set, Butterfly graph, Graph, Planar graph, [INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], Graph bandwidth, Path (graph theory), Cycle graph, symbols, Euler diagram, 020201 artificial intelligence & image processing, Force-directed graph drawing, Multiple edges, Null graph, Algorithm, MathematicsofComputing_DISCRETEMATHEMATICS
Abstract

International audience; PrEd is a force-directed algorithm that improves the existing layout of a graph while preserving its edge crossing properties. The algorithm has a number of applications including: improving the layouts of planar graph drawing algorithms, interacting with a graph layout, and drawing Euler-like diagrams. The algorithm ensures that nodes do not cross edges during its execution. However, PrEd can be computationally expensive and overly-restrictive in terms of node movement. In this paper, we introduce ImPrEd: an improved version of PrEd that overcomes some of its limitations and widens its range of applicability. ImPrEd also adds features such as flexible or crossable edges, allowing for greater control over the output. Flexible edges, in particular, can improve the distribution of graph elements and the angular resolution of the input graph. They can also be used to generate Euler diagrams with smooth boundaries. As flexible edges increase data set size, we experience an execution/drawing quality trade off. However, when flexible edges are not used, ImPrEd proves to be consistently faster than PrEd.

Published
2011

5. Animation, Small Multiples, and the Effect of Mental Map Preservation in Dynamic Graphs

Author

Bruno Pinaud, Helen C. Purchase, Daniel Archambault, Clique Strategic Research Cluster (Clique), University College Dublin [Dublin] (UCD), Department of Computing Science, University of Glasgow, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Graph Visualization and Interactive Exploration (GRAVITE), Université Sciences et Technologies - Bordeaux 1 (UB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), ANR-10-JCJC-0201,EVIDEN,Exploration et VIsualisation de Données rElationnelles dyNamiques(2010), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), and Université Sciences et Technologies - Bordeaux 1-Inria Bordeaux - Sud-Ouest

Subjects
Theoretical computer science, Computer science, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Word error rate, Dynamic Graph Drawing, 02 engineering and technology, Information visualization, Data visualization, 0202 electrical engineering, electronic engineering, information engineering, [INFO]Computer Science [cs], Evaluation, Computer animation, business.industry, 020207 software engineering, Animation, Computer Graphics and Computer-Aided Design, Small Multiples, Mental Map Preservation, Signal Processing, 020201 artificial intelligence & image processing, Algorithm design, Computer Vision and Pattern Recognition, business, Algorithm, Software
Abstract

International audience; In this paper, we present the results of a human computer interaction experiment that compared the performance of the animation of dynamic graphs to the presentation of small multiples and the effect that mental map preservation had on the two conditions. Questions used in the experiment were selected to test both local and global properties of graph evolution over time. The data sets used in this experiment were derived from standard benchmark data sets of the information visualization community. We found that small multiples gave significantly faster performance than animation overall and for each of our five graph comprehension tasks. In addition, small multiples had significantly more errors than animation for the tasks of determining sets of nodes or edges added to the graph during the same timeslice, although a positive time-error correlation coefficient suggests that, in this case, faster responses did not lead to more errors. This result suggests that, for these two tasks, animation is preferable if accuracy is more important than speed. Preserving the mental map under either the animation or the small multiples condition had little influence in terms of error rate and response time.

Published
2011
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6. The readability of Path-Preserving Clusterings of Graphs

Author

Daniel Archambault, Bruno Pinaud, Helen C. Purchase, Graph Visualization and Interactive Exploration (GRAVITE), Université Sciences et Technologies - Bordeaux 1-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Clique Strategic Research Cluster (Clique), University College Dublin [Dublin] (UCD), Department of Computing Science, University of Glasgow, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), G. Melançon and T. Munzner and D. Weiskopf, Université Sciences et Technologies - Bordeaux 1 (UB)-Inria Bordeaux - Sud-Ouest, and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Theoretical computer science, Critical graph, Computer science, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], 02 engineering and technology, Machine learning, computer.software_genre, ACM: G.: Mathematics of Computing/G.2: DISCRETE MATHEMATICS/G.2.2: Graph Theory/G.2.2.0: Graph algorithms, Graph drawing, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, Cluster analysis, 050107 human factors, Null model, business.industry, 05 social sciences, ACM: H.: Information Systems/H.1: MODELS AND PRINCIPLES/H.1.2: User/Machine Systems/H.1.2.0: Human factors, 020207 software engineering, Computer Graphics and Computer-Aided Design, Readability, Graph, Graph (abstract data type), Artificial intelligence, business, computer, Graph product
Abstract

International audience; Graph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be done in a path-preserving way based on attribute values associated with the nodes of the graph. Despite the extensive use these representations, as far as we know, no formal experimentation exists to evaluate if they improve the readability of graphs. In this paper, we present the results of a user study that formally evaluates how such representations affect the readability of graphs. We also explore the effect of graph size and connectivity in terms of this primary research question. Overall, for our tasks, we did not find a significant difference when this clustering is used. However, if the graph is highly connected, these clusterings can improve performance. Also, if the graph is large enough and can be simplified into a few metanodes, benefits in performance on global tasks are realized. Under these same conditions, however, performance of local attribute tasks may be reduced.

Published
2010
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7. Interactive Searching and Visualization of Patterns in Attributed Graphs

Author

Koenig, P. -Y, Zaidi, F., Daniel Archambault, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Graph Visualization and Interactive Exploration (GRAVITE), Université Sciences et Technologies - Bordeaux 1 (UB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Université Sciences et Technologies - Bordeaux 1-Inria Bordeaux - Sud-Ouest, and Zaidi, Faraz

Subjects
[INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR]
Abstract

International audience; Searching for patterns in graphs and visualizing the search results is an active area of research with numerous applications. With the continual growth of database size, querying these databases often results in multiple solutions. Text-based systems present search results as a list, and going over all solutions can be tedious. In this paper, we present an interactive visualization system that helps users find patterns in graphs and visualizes the search results. The user draws a source pattern and labels it with attributes. Based on these attributes and connectivity constraints, simplified subgraphs are generated, containing all the possible solutions. The system is quite generic and capable of searching patterns and approximate solutions in a variety of data sets.

Published
2010

8. Tugging Graphs Faster: Efficiently Modifying Path-Preserving Hierarchies for Browsing Paths

Author

Daniel Archambault, David Auber, Tamara Munzner, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Graph Visualization and Interactive Exploration (GRAVITE), Université Sciences et Technologies - Bordeaux 1 (UB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), University of British Columbia (UBC), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), and Université Sciences et Technologies - Bordeaux 1-Inria Bordeaux - Sud-Ouest

Subjects
Graph Visualization, Theoretical computer science, Graph labeling, Computer science, Distributed computing, Proximity, Information Storage and Retrieval, 02 engineering and technology, Strength of a graph, Simplex graph, User-Computer Interface, Graph drawing, Graph power, Clique-width, 0202 electrical engineering, electronic engineering, information engineering, Computer Graphics, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Graph property, Lattice graph, Complement graph, Forbidden graph characterization, Distance-hereditary graph, Graph Hierarchies, Voltage graph, 020207 software engineering, Graph theory, Computer Graphics and Computer-Aided Design, Graph, Signal Processing, Graph (abstract data type), 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Null graph, Algorithms, Software, MathematicsofComputing_DISCRETEMATHEMATICS
Abstract

International audience; Many graph visualization systems use graph hierarchies to organize a large input graph into logical components. These approaches detect features globally in the data and place these features inside levels of a hierarchy. However, this feature detection is a global process and does not consider nodes of the graph near a feature of interest. TugGraph is a system for exploring paths and proximity around nodes and subgraphs in a graph. The approach modifies a pre-existing hierarchy in order to see how a node or subgraph of interest extends out into the larger graph. It is guaranteed to create path-preserving hierarchies, so that the abstraction shown is meaningful with respect to the underlying structure of the graph. The system works well on graphs of hundreds of thousands of nodes and millions of edges. TugGraph is able to present views of this proximal information in the context of the entire graph in seconds, and does not require a layout of the full graph as input.

Published
2010

9. Fully Automatic Visualisation of Overlapping Sets

Author

David Auber, Daniel Archambault, Paolo Simonetto, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Graph Visualization and Interactive Exploration (GRAVITE), Université Sciences et Technologies - Bordeaux 1 (UB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Systryp, Gravité, Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Université Sciences et Technologies - Bordeaux 1-Inria Bordeaux - Sud-Ouest, and Simonetto, Paolo

Subjects
Hierarchy, Theoretical computer science, Hierarchy (mathematics), Computer science, 020207 software engineering, Bézier curve, 02 engineering and technology, computer.file_format, computer.software_genre, Computer Graphics and Computer-Aided Design, Visualization, I.3.3 [Picture/Image Generation]: Display algorithms, symbols.namesake, Tree (data structure), [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Fully automatic, 0202 electrical engineering, electronic engineering, information engineering, symbols, Euler diagram, 020201 artificial intelligence & image processing, Executable, Data mining, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], computer
Abstract

International audience; Visualisation of taxonomies and sets has recently become an active area of research. Many application fields now require more than a strict classification of elements into a hierarchy tree. Euler diagrams, one of the most natural ways of depicting intersecting sets, may provide a solution to these problems. In this paper, we present an approach for the automatic generation of Euler-like diagrams. This algorithm differs from previous approaches in that it has no undrawable instances of input, allowing it to be used in systems where the output is always required. We also improve the readability of Euler diagrams through the use of Bezier curves and transparent coloured textures. Our approach has been implemented using the Tulip platform. Both the source and executable program used to generate the results are freely available.

Published
2009

10. Solving the Traffic and Flitter Challenges with Tulip

Author

Jonathan Dubois, Daniel Archambault, M. Brulin, Pierre-Yves Koenig, R. Vieux, Ronan Sicre, Trung-Tien Phan-Quang, Guy Melançon, F. Gilbert, Morgan Mathiaut, Antoine Lambert, Paolo Simonetto, Faraz Zaidi, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Graph Visualization and Interactive Exploration (GRAVITE), Université Sciences et Technologies - Bordeaux 1-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Gravité, Simonetto, Paolo, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), and Université Sciences et Technologies - Bordeaux 1 (UB)-Inria Bordeaux - Sud-Ouest

Subjects
060201 languages & linguistics, Geospatial analysis, Social network, business.industry, Computer science, Graph theory, 06 humanities and the arts, 02 engineering and technology, CONTEST, computer.software_genre, Visualization, World Wide Web, Data visualization, Encoding (memory), [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0602 languages and literature, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], business, Business communication, computer, H.5.0 [Information Systems]: Information Interfaces and Presentation — F.2.2 [Theory of Computation]: Nonnumerical Algorithms and Problems—Pattern matching
Abstract

International audience; We present our visualization systems and findings for the Badge and Network Traffic as well as the Social Network and Geospatial challenges of the 2009 VAST contest. The summary starts by presenting an overview of our time series encoding of badge information and network traffic. Our findings suggest that employee 30 may be of interest. In the second part of the paper, we describe our system for finding subgraphs in the social network subject to degree constraints. Subsequently, we present our most likely candidate network which is similar to scenario B.

Published
2009

11. Structural Differences Between Two Graphs through Hierarchies

Author

Daniel Archambault, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Graph Visualization and Interactive Exploration (GRAVITE), Université Sciences et Technologies - Bordeaux 1 (UB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Université Sciences et Technologies - Bordeaux 1-Inria Bordeaux - Sud-Ouest, and Archambault, Daniel

Subjects
ACM: H.: Information Systems/H.5: INFORMATION INTERFACES AND PRESENTATION (e.g., HCI)/H.5.0: General, ACM: H.: Information Systems, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], ACM: G.: Mathematics of Computing/G.2: DISCRETE MATHEMATICS/G.2.2: Graph Theory/G.2.2.0: Graph algorithms
Abstract

International audience; This paper presents a technique for visualizing the differences between two graphs. The technique assumes that a unique labeling of the nodes for each graph is available, where if a pair of labels match, they correspond to the same node in both graphs. Such labeling often exists in many application areas: IP addresses in computer networks, namespaces, class names, and function names in software engineering, to name a few. As many areas of the graph may be the same in both graphs, we visualize large areas of difference through a graph hierarchy. We introduce a path-preserving coarsening technique for degree one nodes of the same classification. We also introduce a path-preserving coarsening technique based on betweenness centrality that is able to illustrate major differences between two graphs.

Published
2009

12. TugGraph: Path-Preserving Hierarchies for Browsing Proximity and Paths in Graphs

Author

Tamara Munzner, David Auber, Daniel Archambault, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Graph Visualization and Interactive Exploration (GRAVITE), Université Sciences et Technologies - Bordeaux 1 (UB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), University of British Columbia (UBC), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), and Université Sciences et Technologies - Bordeaux 1-Inria Bordeaux - Sud-Ouest

Subjects
ACM: H.: Information Systems/H.5: INFORMATION INTERFACES AND PRESENTATION (e.g., HCI)/H.5.0: General, Graph Visualization, Theoretical computer science, Graph Hierarchies, Computer science, Distributed computing, Proximity, Voltage graph, 020207 software engineering, 02 engineering and technology, Strength of a graph, ACM: G.: Mathematics of Computing/G.2: DISCRETE MATHEMATICS/G.2.2: Graph Theory/G.2.2.0: Graph algorithms, Butterfly graph, ACM: H.: Information Systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Null graph, Lattice graph, Graph property, Complement graph, Distance-hereditary graph, MathematicsofComputing_DISCRETEMATHEMATICS
Abstract

International audience; Many graph visualization systems use graph hierarchies to organize a large input graph into logical components. These approaches detect features globally in the data and place these features inside levels of a hierarchy. However, this feature detection is a global process and does not consider nodes of the graph near a feature of interest. TugGraph is a system for exploring paths and proximity around nodes and subgraphs in a graph. The approach modifies a pre-existing hierarchy in order to see how a node or subgraph of interest extends out into the larger graph. It is guaranteed to create path-preserving hierarchies, so that the abstraction shown is meaningful with respect to the structure of the graph. The system works well on graphs of hundreds of thousands of nodes and millions of edges. TugGraph is able to present views of this proximal information in the context of the entire graph in seconds, and does not require a layout of the full graph as input.

Published
2009

13. Smashing Peacocks Further: Drawing Quasi-Trees from Biconnected components

Author

Tamara Munzner, David Auber, Daniel Archambault, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), and Auber, David

Subjects
Block graph, SPQR tree, Spanning tree, Theoretical computer science, K-ary tree, Computer science, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Biconnected component, 020207 software engineering, 02 engineering and technology, Directed graph, Computer Graphics and Computer-Aided Design, Tree (graph theory), Biconnected graph, Graph, Combinatorics, [INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], Tree structure, Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Software, ComputingMilieux_MISCELLANEOUS, MathematicsofComputing_DISCRETEMATHEMATICS
Abstract

Quasi-trees, namely graphs with tree-like structure, appear in many application domains, including bioinformatics and computer networks. Our new SPF approach exploits the structure of these graphs with a two-level approach to drawing, where the graph is decomposed into a tree of biconnected components. The low-level biconnected components are drawn with a force-directed approach that uses a spanning tree skeleton as a starting point for the layout. The higher-level structure of the graph is a true tree with meta-nodes of variable size that contain each biconnected component. That tree is drawn with a new area-aware variant of a tree drawing algorithm that handles high-degree nodes gracefully, at the cost of allowing edge-node overlaps. SPF performs an order of magnitude faster than the best previous approaches, while producing drawings of commensurate or improved quality.

Published
2006

14. Node Overlap Removal Algorithms: A Comparative Study

Author

Chen, Fati, Piccinini, Laurent, Poncelet, Pascal, Sallaberry, Arnaud, Université Paul-Valéry - Montpellier 3 (UPVM), ADVanced Analytics for data SciencE (ADVANSE), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), ANR Daphne 17-CE28-0013-01, Daniel Archambault, and Csaba D. Tóth

Subjects
Graph drawing, Node overlap removal, ACM: H.: Information Systems/H.5: INFORMATION INTERFACES AND PRESENTATION (e.g., HCI)/H.5.2: User Interfaces, ACM: I.: Computing Methodologies/I.3: COMPUTER GRAPHICS/I.3.5: Computational Geometry and Object Modeling, 68R10 (Primary), 68U05 (Secondary), [INFO]Computer Science [cs], Layout adjustment, [INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG]
Abstract

Appears in the Proceedings of the 27th International Symposium on Graph Drawing and Network Visualization (GD 2019); International audience; Many algorithms have been designed to remove node overlapping, and many quality criteria and associated metrics have been proposed to evaluate those algorithms. Unfortunately, a complete comparison of the algorithms based on some metrics that evaluate the quality has never been provided and it is thus difficult for a visualization designer to select the algorithm that best suits his needs. In this paper, we review 21 metrics available in the literature, classify them according to the quality criteria they try to capture, and select a representative one for each class. Based on the selected metrics, we compare 8 node overlap removal algorithms. Our experiment involves 854 synthetic and real-world graphs.

Published
2019
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