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2015 International Conference on Big Data and Smart Computing (BigComp) (2015)
Jeju, South Korea
Feb. 9, 2015 to Feb. 11, 2015
ISBN: 978-1-4799-7303-3
pp: 271-278
Robert Pienta , Georgia Institute of Technology
James Abello , Rutgers University
Minsuk Kahng , Georgia Institute of Technology
Duen Horng Chau , Georgia Institute of Technology
Making sense of large graph datasets is a fundamental and challenging process that advances science, education and technology. We survey research on graph exploration and visualization approaches aimed at addressing this challenge. Different from existing surveys, our investigation highlights approaches that have strong potential in handling large graphs, algorithmically, visually, or interactively; we also explicitly connect relevant works from multiple research fields — data mining, machine learning, human-computer ineraction, information visualization, information retrieval, and recommender systems — to underline their parallel and complementary contributions to graph sensemaking. We ground our discussion in sensemaking research; we propose a new graph sensemaking hierarchy that categorizes tools and techniques based on how they operate on the graph data (e.g., local vs global). We summarize and compare their strengths and weaknesses, and highlight open challenges. We conclude with future research directions for graph sensemaking.
Data visualization, Visualization, Data mining, Scalability, Pattern matching, Machine learning algorithms, Algorithm design and analysis

R. Pienta, J. Abello, M. Kahng and D. H. Chau, "Scalable graph exploration and visualization: Sensemaking challenges and opportunities," 2015 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Jeju, South Korea, 2015, pp. 271-278.
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