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  • 1.
    Jusufi, Ilir
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Liu, Jiayi
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Zimmer, Björn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Visual Exploration of Relationships between Document Clusters2014In: IVAPP 2014: Proceedings od the 5th International Conference on Information Visualization Theory and Applications / [ed] Robert S. Laramee, Andreas Kerren, José Braz, SciTePress, 2014, p. 195-203Conference paper (Refereed)
    Abstract [en]

    The visualization of networks with additional attributes attached to the network elements is one of the ongoing challenges in the information visualization domain. Such so-called multivariate networks regularly appear in various application fields, for instance, in data sets which describe friendship networks or co-authorship networks. Here, we focus on networks that are based on text documents, i.e., the network nodes represent documents and the edges show relationships between them. Those relationships can be derived from common topics or common co-authors. Attached attributes may be specific keywords (topics), keyword frequencies, etc. The analysis of such multivariate networks is challenging, because a deeper understanding of the data provided depends on effective visualization and interaction techniques that are able to bring all types of information together. In addition, automatic analysis methods should be used to support the analysis process of potentially large amounts of data. In this paper, we present a visualization approach that tackles those analysis problems. Our implementation provides a combination of new techniques that shows intra-cluster and inter-cluster relations while giving insight into the content of the cluster attributes. Hence, it facilitates the interactive exploration of the networks under consideration by showing the relationships between node clusters in context of network topology and multivariate attributes.

  • 2.
    Jusufi, Ilir
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Zimmer, Björn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Multivariate Network Exploration with JauntyNets2013In: Proceedings 2013 17th International Conference on Information Visualisation IV 2013: 16-18 July 2013, London, United Kingdom, IEEE, 2013, p. 19-27Conference paper (Refereed)
    Abstract [en]

    The amount of data produced in the world every day implies a huge challenge in understanding and extracting knowledge from it. Much of this data is of relational nature, such as social networks, metabolic pathways, or links between software components. Traditionally, those networks are represented as node-link diagrams or matrix representations. They help us to understand the structure (topology) of the relational data. However in many real world data sets, additional (often multidimensional) attributes are attached to the network elements. One challenge is to show these attributes in context of the underlying network topology in order to support the user in further analyses. In this paper, we present a novel approach that extends traditional force-based graph layouts to create an attribute-driven layout. In addition, our prototype implementation supports interactive exploration by introducing clustering and multidimensional scaling into the analysis process.

  • 3.
    Kerren, Andreas
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Köstinger, Harald
    Zimmer, Björn
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    ViNCent: Visualization of Network Centralities2012In: Proceedings of the International Conference on Information Visualization Theory and Applications (IVAPP '12), SciTePress , 2012, p. 703-712Conference paper (Refereed)
    Abstract [en]

    The use of network centralities in the field of network analysis plays an important role when the relative importanceof nodes within the network topology should be rated. A single network can easily be represented by theuse of standard graph drawing algorithms, but not only the exploration of one centrality might be important:the comparison of two or more of them is often crucial for a better understanding. When visualizing the comparisonof several network centralities, we are facing new problems of how to show them in a meaningful way.For instance, we want to be able to track all the changes of centralities in the networks as well as to displaythe single networks as best as possible. In the life sciences, centrality measures help scientists to understandthe underlying biological processes and have been successfully applied to different biological networks. Theaim of this paper is to present a novel system for the interactive visualization of biochemical networks and itscentralities. Researchers can focus on the exploration of the centrality values including the network structurewithout dealing with visual clutter or occlusions of nodes. Simultaneously, filtering based on statistical dataconcerning the network elements and centrality values supports this.

  • 4.
    Wybrow, Michael
    et al.
    Monash University Caulfield, Australia.
    Elmqvist, Niklas
    Purdue University, USA.
    Fekete, Jean-Daniel
    INRIA, France.
    von Landesberger, Tatiana
    Technische Universität Darmstadt, Germany.
    van Wijk, Jarke J.
    Eindhoven University of Technology, The Netherlands.
    Zimmer, Björn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Interaction in the visualization of multivariate networks2014In: Towards Multivariate Network Visualization: 3rd Dagstuhl Seminar on Information Visualization, Germany, May 12-17, 2013. Revised Discussions / [ed] Andreas Kerren, Helen C. Purchase, Matthew O. Ward, Springer, 2014, p. 97-125Conference paper (Refereed)
    Abstract [en]

    The overall aim of visualization is to obtain insight into large amounts of data. Detection of patterns as well as outliers are typical examples. For networks, such patterns can be number and position of cliques; for multivariate data this can be the correlation between attributes. The major challenge of multivariate network visualization is to understand the interplay between properties of the network and its associated data, for instance to see if the formation of cliques can be understood from attributes of nodes.

  • 5.
    Zimmer, Björn
    et al.
    University of Applied Sciences and Arts Hannover, Germany.
    Ackermann, Dennie
    University of Applied Sciences and Arts Hannover, Germany.
    Schröder, Manfred
    HFN Medien GmbH, Ehlbeek 3, 30938, Burgwedel, Germany.
    Kerren, Andreas
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Ahlers, Volker
    University of Applied Sciences and Arts Hannover, Germany.
    Comparative Visualization of User Flows in Voice Portals2011In: Graph Drawing: 18th International Symposium, GD 2010, Konstanz, Germany, September 21-24, 2010. Revised Selected Papers, Berlin Heidelberg: Springer, 2011, p. 404-405Chapter in book (Refereed)
    Abstract [en]

    Voice portals are widely used to guide users interactively through an application. Recent portals provide a growing number of functions in one application, thus increasing their complexity. This work presents flow-map-based techniques for the comparative visualization of user flows at different time frames, in order to enable dialog designers to analyze and improve the user interaction with these systems.

    Natural Language Systems in Voice Portals: More sophisticated voice portals use natural language systems (NLS), giving users the option to actually “talk” to the system in whole sentences. The system tries to interpret these sentences and interactively asks the user for detailed information, if necessary. Portals using NLS are rather large and complex, making it difficult to analyze their performance. Especially after applying changes to a voice portal or in case of technical problems, it is important to be able to analyze the consequences on user flows in the system.

  • 6.
    Zimmer, Björn
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Jusufi, Ilir
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Kerren, Andreas
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Analyzing Multiple Network Centralities with ViNCent2012In: Proceedings of SIGRAD 2012: Interactive Visual Analysis of Data, November 29-30, 2012, Växjö, Sweden, / [ed] Andreas Kerren and Stefan Seipel, Linköping: Linköping University Electronic Press, 2012, p. 87-90Conference paper (Refereed)
    Abstract [en]

    The analysis of multivariate networks is an important task in various application domains, such as social networkanalysis or biochemistry. In this paper, we address the interactive visual analysis of the results of centralitycomputations in context of networks. An important analytical aspect is to examine nodes according to specific centralityvalues and to compare them. We present a tool that combines exploratory data visualization with automaticanalysis techniques, such as computing a variety of centrality values for network nodes as well as hierarchicalclustering or node reordering based on centrality values. Automatic and interactive approaches are seamlesslyintegrated in one single tool which provides insight into the importance of an individual node or groups of nodesand allows quantifying the network structure.

  • 7.
    Zimmer, Björn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Applying Heat Maps in a Web-Based Collaborative Graph Visualization2014In: Poster Abstracts of IEEE VIS 2014, 2014Conference paper (Refereed)
    Abstract [en]

    The visual analysis of large and complex networks is a challenging task in many fields, such as systems biology or social sciences. Often, various domain experts work together to improve the analysis time or the quality of the analysis results. Collaborative visualization tools can facilitate this process. We propose a new web-based visualization environment which supports distributed, synchronous and asynchronous collaboration for graphs with up to 10,000 nodes and edges. In addition to standard collaboration features like event tracking or synchronizing, our client/server-based system provides visualization and interaction techniques for better navigation, guidance and overview of the overall data set. During asynchronous collaborations, network changes made by specific analysts or even just visited elements are highlighted on demand by heat maps. These heat map representations are user-sensitive in a sense that the current analyst is able to perceive which changes were made by others. 

  • 8.
    Zimmer, Björn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Displaying User Behavior in the Collaborative Graph Visualization System OnGraX2015In: Graph Drawing and Network Visualization: 23rd International Symposium on Graph Drawing and Network Visualization, GD 2015, Los Angeles, CA, USA, September 24-26, 2015, Revised Selected Papers / [ed] Emilio Di Giacomo; Anna Lubiw, Springer, 2015, p. 247-259Chapter in book (Refereed)
    Abstract [en]

    The visual analysis of complex networks is a challenging task in many fields, such as systems biology or social sciences. Often, various domain experts work together to improve the analysis time or the quality of the analysis results. Collaborative visualization tools can facilitate the analysis process in such situations. We propose a new web-based visualization environment which supports distributed, synchronous and asynchronous collaboration. In addition to standard collaboration features like event tracking or synchronizing, our client/server-based system provides a rich set of visualization and interaction techniques for better navigation and overview of the input network. Changes made by specific analysts or even just visited network elements are highlighted on demand by heat maps. They enable us to visualize user behavior data without affecting the original graph visualization. We evaluate the usability of the heat map approach against two alternatives in a user experiment.

  • 9.
    Zimmer, Björn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Harnessing WebGL and WebSockets for a Web-Based Collaborative Graph Exploration Tool2015In: Engineering the Web in the Big Data Era: 15th International Conference, ICWE 2015, Rotterdam, The Netherlands, June 23-26, 2015, Proceedings / [ed] Philipp Cimiano, Flavius Frasincar, Geert-Jan Houben, and Daniel Schwabe, Springer, 2015, p. 583-598Conference paper (Refereed)
    Abstract [en]

    The advancements of web technologies in recent years made it possible to switch from traditional desktop software to online solutions. Today, people naturally use web applications to work together on documents, spreadsheets, or blogs in real time. Also interactive data visualizations are more and more shared in the web. They are thus easily accessible, and it is possible to collaboratively discuss and explore complex data sets. A still open problem in collaborative information visualization is the online exploration of node-link diagrams of graphs (or networks) in fields such as social sciences or systems biology. In this paper, we address challenges related to this research problem and present a client/server-based visualization system for the collaborative exploration of graphs. Our approach uses WebGL to render large graphs in a web application and provides tools to coordinate the analysis process of multiple users in synchronous as well as asynchronous sessions. 

  • 10.
    Zimmer, Björn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    OnGraX: A Web-Based System for the Collaborative Visual Analysis of Graphs2017In: Journal of Graph Algorithms and Applications, ISSN 1526-1719, E-ISSN 1526-1719, Vol. 21, no 1, p. 5-27Article in journal (Refereed)
    Abstract [en]

    The visual analysis of complex networks is a challenging task in many fields, such as systems biology or social sciences. Often, various domain experts work together to improve the analysis time or the quality of the analysis results. Collaborative visualization tools can facilitate the analysis process in such situations. We propose a new web-based visualization environment which supports distributed, synchronous and asynchronous collaboration. In addition to standard collaboration features like event tracking or synchronizing, our client/server-based system provides a rich set of visualization and interaction techniques for better navigation and overview of the input network. Changes made by specific analysts or even just visited network elements are highlighted on demand by heat maps. They enable us to visualize user behavior data without affecting the original graph visualization, are robust against layout changes, and are user-sensitive in a sense that the current analyst is able to perceive which changes were made by others in asynchronous collaboration. In case of synchronous collaboration, an analyst can see where and what others are currently analyzing in the network visualization. Thus, our approach addresses critical collaborative visualization challenges, for instance, awareness and coordination of user activities or pointing to interesting objects. We evaluated the usability of the heat map approach against two alternatives in a controlled user experiment. In addition, the results of a domain expert review are described in this article.

  • 11.
    Zimmer, Björn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Sensemaking and Provenance in Distributed Collaborative Node-Link Visualizations2014Conference paper (Refereed)
    Abstract [en]

    Various experts often work together during the analysis of large and complex data sets in order to minimize the required time and to improve the quality of the analysis results. Keeping track of the reasoning involved during a collaborative process and using this information later to review and reflect upon it can be a challenging task. For instance, analysts should have the possibility to quickly review changes performed on a graph and get an idea of the most interesting regions according to the user history without the need to replay every single action that was performed by prior users. This paper focuses on challenges during the collection and visualization of the sensemaking process in distributed collaborative node-link visualizations. We raise five challenges that we think need discussion among researchers in this domain and present our tool OnGraX—a web-based collaborative tool for analyzing networks—that addresses some of those challenges.

  • 12.
    Zimmer, Björn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Sahlgren, Magnus
    Swedi Swedish Research Institute (RISE SICS).
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Visual Analysis of Relationships between Heterogeneous Networks and Texts: An Application on the IEEE VIS Publication Dataset2017In: Informatics, ISSN 2227-9709, Vol. 4, no 2, article id 11Article in journal (Refereed)
    Abstract [en]

    The visual exploration of large and complex network structures remains a challenge for many application fields. Moreover, a growing number of real world networks are multivariate and often interconnected with each other. Entities in a network may have relationships with elements of other related data sets, which do not necessarily have to be networks themselves, and these relationships may be defined by attributes that can vary greatly. In this work, we propose a comprehensive visual analytics approach that supports researchers to specify and subsequently explore attribute-based relationships across networks, text documents, and derived secondary data. Our approach provides an individual search functionality based on keywords and semantically similar terms over the entire text corpus to find related network nodes. For examining these nodes in the interconnected network views, we introduce a new interaction technique, called Hub2Go, which facilitates the navigation by guiding the user to the information of interest. To showcase our system, we use a large text corpus collected from research papers listed in the IEEE VIS publications dataset that consists of 2752 documents over a period of 25 years. Here, we analyze relationships between various heterogeneous networks, a Bag-of-Words index, and a word similarity matrix, all derived from the initial corpus and metadata. 

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