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Manolis Savva

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Publications by Manolis Savva (bibliography)

2012

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Kairam, Sanjay, MacLean, Diana, Savva, Manolis and Heer, Jeffrey (2012): GraphPrism: compact visualization of network structure. In: Proceedings of the 2012 International Conference on Advanced Visual Interfaces 2012. pp. 498-505.

“Visual methods for supporting the characterization, comparison, and classification of large networks remain an open challenge. Ideally, such techniques should surface useful structural features -- such as effective diameter, small-world properties, and structural holes -- not always apparent from either summary statistics or typical network visualizations. In this paper, we present GraphPrism, a technique for visually summarizing arbitrarily large graphs through combinations of 'facets', each corresponding to a single node- or edge-specific metric (e.g., transitivity). We describe a generalized approach for constructing facets by calculating distributions of graph metrics over increasingly large local neighborhoods and representing these as a stacked multi-scale histogram. Evaluation with paper prototypes shows that, with minimal training, static GraphPrism diagrams can aid network analysis experts in performing basic analysis tasks with network data. Finally, we contribute the design of an interactive system using linked selection between GraphPrism overviews and node-link detail views. Using a case study of data from a co-authorship network, we illustrate how GraphPrism facilitates interactive exploration of network data.”

2011

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Savva, Manolis, Kong, Nicholas, Chhajta, Arti, Fei-Fei, Li, Agrawala, Maneesh and Heer, Jeffrey (2011): ReVision: automated classification, analysis and redesign of chart images. In: Proceedings of the 2011 ACM Symposium on User Interface Software and Technology 2011. pp. 393-402.

“Poorly designed charts are prevalent in reports, magazines, books and on the Web. Most of these charts are only available as bitmap images; without access to the underlying data it is prohibitively difficult for viewers to create more effective visual representations. In response we present ReVision, a system that automatically redesigns visualizations to improve graphical perception. Given a bitmap image of a chart as input, ReVision applies computer vision and machine learning techniques to identify the chart type (e.g., pie chart, bar chart, scatterplot, etc.). It then extracts the graphical marks and infers the underlying data. Using a corpus of images drawn from the web, ReVision achieves image classification accuracy of 96% across ten chart categories. It also accurately extracts marks from 79% of bar charts and 62% of pie charts, and from these charts it successfully extracts data from 71% of bar charts and 64% of pie charts. ReVision then applies perceptually-based design principles to populate an interactive gallery of redesigned charts. With this interface, users can view alternative chart designs and retarget content to different visual styles.”

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