The Network Lens: Interactive Exploration of Multivariate Networks Using Visual Filtering

Jusufi, Ilir; Dingjie, Yang; Kerren, Andreas

doi:10.1109/iv.2010.15

Cited by 24 publications

(15 citation statements)

References 14 publications

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“…Jusufi et al [36] describe lenses for multivariate network that display nodes as small multidimensional visualizations when they are within the focal area. They use several visualizations: parallel coordinates, bar charts, and star plots.…”

Section: Brushing and Linkingmentioning

confidence: 99%

Interaction in the Visualization of Multivariate Networks

Wybrow¹,

Elmqvist²,

Fekete³

et al. 2014

Multivariate Network Visualization

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Abstract. Interaction is a vital component in the visualization of multivariate networks. It enables greater amounts of information to be seen and explored than is possible with static visualization. Interaction can also help show the information landscape of the data while still allowing users to find and view areas of interest in greater detail and pivot between these. In this chapter we first discuss the design space and requirements for interacting with large multivariate data sets. We describe and classify relevant interaction techniques, and give examples of the interactive aspects of multivariate graph visualization systems. We present recommendations and guidelines for designing novel interaction approaches. Finally, we describe the open challenges within the field of multivariate graph visualization as we see them.

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Section: Brushing and Linkingmentioning

confidence: 99%

Interaction in the Visualization of Multivariate Networks

Wybrow¹,

Elmqvist²,

Fekete³

et al. 2014

Multivariate Network Visualization

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“…Each diagram represents experimental data that is related to the node. To handle the clutter that could be introduced if the embedded diagrams cause too many overlaps, magic lenses can be used to show different multivariate attributes on demand [Jusufi et al, 2010].…”

Section: Related Workmentioning

confidence: 99%

Visual Exploration of Relationships between Document Clusters

Jusufi

Kerren

Liu

et al. 2014

Proceedings of the 5th International Conference on Information Visualization Theory and Applications

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Abstract: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.

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“…Some of them are more general systems or toolkits [3,4,6,7,9,12,15,18,27,26,32,14,16,34], while others are specialized for a specific task or analysis [ 8,20,22]. NodeXL [14] is an exploration tool that is integrated into Excel.…”

Section: Network/graph Visual Analytic Toolsmentioning

confidence: 99%

A process-centric data mining and visual analytic tool for exploring complex social networks

Denis

Singh

Mann

2013

Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics

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Social scientists and observational scientists have a need to analyze complex network data sets. Examples of such exploratory tasks include: finding communities that exist in the data, comparing results from different graph mining algorithms, identifying regions of similarity or dissimilarity in the data sets, and highlighting nodes with important centrality properties. While many methods, algorithms, and visualizations exist, the capability to apply and combine them for ad-hoc visual exploration or as part of an analytic workflow process is still an open problem that needs to be addressed to help scientists, especially those without extensive programming knowledge. In this paper, we present Invenio-Workflow, a tool that supports exploratory analysis of network data by integrating workflow, querying, data mining, statistics, and visualization to enable scientific inquiry. Invenio-Workflow can be used to create custom exploration tasks, in addition to the standard task templates. After describing the features of the system, we illustrate its utility through several use cases based on networks from different domains.

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The Network Lens: Interactive Exploration of Multivariate Networks Using Visual Filtering

Cited by 24 publications

References 14 publications

Interaction in the Visualization of Multivariate Networks

Interaction in the Visualization of Multivariate Networks

Visual Exploration of Relationships between Document Clusters

A process-centric data mining and visual analytic tool for exploring complex social networks

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