MatrixExplorer: a Dual-Representation System to Explore Social Networks

Henry, Nathalie; Fekete, Jean-Daniel

doi:10.1109/tvcg.2006.160

Cited by 230 publications

(187 citation statements)

References 24 publications

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“…Beyond these domain-specific design requirements, we determined that common network-related analysis tasks such as those identified in the taxonomy [30] and exemplified in systems such as Vizster [21], SocialAction [36], and MatrixExplorer [22] should be supported. Furthermore, adopting the "Search, Show Context, Expand on Demand" design model [44] seemed appropriate for the system.…”

Section: Design Requirementsmentioning

confidence: 99%

Understanding Interfirm Relationships in Business Ecosystems with Interactive Visualization

Basole¹,

Clear²,

Hu³

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. The dotlink360 user interface including the navigator panel (left), the central visualization window, and the details panel (right).This image shows the Segment view with Apple selected so it and its market segments are colored yellow, while its partners are colored orange. The mouse cursor is over Nokia so it and its segments are colored light blue, with its partners dark blue. Company positions are determined by their segment memberships.Abstract-Business ecosystems are characterized by large, complex, and global networks of firms, often from many different market segments, all collaborating, partnering, and competing to create and deliver new products and services. Given the rapidly increasing scale, complexity, and rate of change of business ecosystems, as well as economic and competitive pressures, analysts are faced with the formidable task of quickly understanding the fundamental characteristics of these interfirm networks. Existing tools, however, are predominantly query-or list-centric with limited interactive, exploratory capabilities. Guided by a field study of corporate analysts, we have designed and implemented dotlink360, an interactive visualization system that provides capabilities to gain systemic insight into the compositional, temporal, and connective characteristics of business ecosystems. dotlink360 consists of novel, multiple connected views enabling the analyst to explore, discover, and understand interfirm networks for a focal firm, specific market segments or countries, and the entire business ecosystem. System evaluation by a small group of prototypical users shows supporting evidence of the benefits of our approach. This design study contributes to the relatively unexplored, but promising area of exploratory information visualization in market research and business strategy.

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Section: Design Requirementsmentioning

confidence: 99%

Understanding Interfirm Relationships in Business Ecosystems with Interactive Visualization

Basole¹,

Clear²,

Hu³

et al. 2013

IEEE Trans. Visual. Comput. Graphics

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show abstract

“…The community has studied this problem at length. In [16], a comprehensive survey on automatic vertex order is included. In general, binary, undirected graphs are the most straightforward.…”

Section: The Interactive Approachmentioning

confidence: 99%

“…In addition, to let users decide about uncertainty issues, we would like to avoid taking a threshold at this stage of processing due to possible information loss. This makes it hard to directly leverage existing vertex reordering algorithms like those surveyed by Mueller [25] or Henry et al [16].…”

Section: A Clutter-free Interface For Graph Abstractionmentioning

confidence: 99%

Dynamic Visualization of Coexpression in Systems Genetics Data

Kendall

Huang

et al. 2008

IEEE Trans. Visual. Comput. Graphics

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Abstract-Biologists hope to address grand scientific challenges by exploring the abundance of data made available through microarray analysis and other high-throughput techniques. However, the impact of this large volume of data is limited unless researchers can effectively assimilate the entirety of this complex information and integrate it into their daily research; interactive visualization tools are called for to support the effort. Specifically, typical studies of gene co-expression can make use of novel visualization tools that enable the dynamic formulation and fine-tuning of hypotheses to aid the process of evaluating sensitivity of key parameters and achieving data reduction. These tools should allow biologists to develop an intuitive understanding of the structure of biological networks and discover genes which reside in critical positions in networks and pathways. By using a graph as a universal data representation of correlation in gene expression data, our novel visualization tool employs several techniques that when used in an integrated manner provide innovative analytical capabilities. Our tool for interacting with gene co-expression data integrates techniques such as: graph layout, qualitative subgraph extraction through a novel 2D user interface, quantitative subgraph extraction using graph-theoretic algorithms or by querying an optimized b-tree, dynamic level-of-detail graph abstraction, and template-based fuzzy classification using neural networks. We demonstrate our system using a real-world workflow from a large-scale, systems genetics study of mammalian gene co-expression.

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“…Adjacency matrix representations, such as the MatrixExplorer (Henry & Fekete, 2006) and the Matrix Browser (Ziegler, Kunz, & Botsch, 2002), show relationships between items, typically link relationships between nodes in a graph. These adjacency matrices are of order n x n for an n node graph.…”

Section: Related Workmentioning

confidence: 99%

Exploratory Data Analysis With Categorical Variables: An Improved Rank-by-Feature Framework and a Case Study

Seo¹,

Gordish‐Dressman²

2007

International Journal of Human-Computer Interaction

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Multidimensional data sets often include categorical information. When most dimensions have categorical information, clustering the data set as a whole can reveal interesting patterns in the data set. However, the categorical information is often more useful as a way to partition the data set: gene expression data for healthy versus diseased samples or stock performance for common, preferred, or convertible shares. We present novel ways to utilize categorical information in exploratory data analysis by enhancing the rank-by-feature framework. First, we present ranking criteria for categorical variables and ways to improve the score overview. Second, we present a novel way to utilize the categorical information together with clustering algorithms. Users can partition the data set according to categorical information vertically or horizontally, and the clustering result for each partition can serve as new categorical information. We report the results of a longitudinal case study with a biomedical research team, including insights gained and potential future work.

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MatrixExplorer: a Dual-Representation System to Explore Social Networks

Cited by 230 publications

References 24 publications

Understanding Interfirm Relationships in Business Ecosystems with Interactive Visualization

Understanding Interfirm Relationships in Business Ecosystems with Interactive Visualization

Dynamic Visualization of Coexpression in Systems Genetics Data

Exploratory Data Analysis With Categorical Variables: An Improved Rank-by-Feature Framework and a Case Study

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