Scalable graph exploration and visualization: Sensemaking challenges and opportunities

Pienta, Robert; Abello, James; Kahng, Minsuk; Chau, Duen Horng

doi:10.1109/35021bigcomp.2015.7072812

Cited by 70 publications

(44 citation statements)

References 71 publications

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“…Originating in concepts developed in the cognitive sciences, recent works aim at studying and modeling this workflow, in order to adapt interactive tools to better support it [Endert et al 2014a;Endert et al 2014b;Sun et al 2014;Pienta et al 2015].…”

Section: Provenance and Sensemakingmentioning

confidence: 99%

Mining Redescriptions with Siren

Galbrun

Miettinen

2018

ACM Trans. Knowl. Discov. Data

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In many areas of science, scientists need to find distinct common characterizations of the same objects and, vice versa, to identify sets of objects that admit multiple shared descriptions. For example, in biology, an important task is to identify the bioclimatic constraints that allow some species to survive, that is, to describe geographical regions both in terms of the fauna that inhabits them and of their bioclimatic conditions. In data analysis, the task of automatically generating such alternative characterizations is called redescription mining.If a domain expert wants to use redescription mining in his research, merely being able to find redescriptions is not enough. He must also be able to understand the redescriptions found, adjust them to better match his domain knowledge, test alternative hypotheses with them, and guide the mining process towards results he considers interesting. To facilitate these goals, we introduce Siren, an interactive tool for mining and visualizing redescriptions.Siren allows to obtain redescriptions in an anytime fashion through efficient, distributed mining, to examine the results in various linked visualizations, to interact with the results either directly or via the visualizations, and to guide the mining algorithm toward specific redescriptions. In this paper, we explain the features of Siren and why they are useful for redescription mining. We also propose two novel redescription mining algorithms that improve the generalizability of the results compared to the existing ones.

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Section: Provenance and Sensemakingmentioning

confidence: 99%

Mining Redescriptions with Siren

Galbrun

Miettinen

2018

ACM Trans. Knowl. Discov. Data

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“…The visualization community has also investigated local graph exploration to handle very large graphs [30,1,36,24]. Bottom-up exploration first appeared in [12], a tool for exploring hierarchies using a "degree of interest" (DOI) function to rank the relevance of currently undisplayed nodes.…”

Section: Degree Of Interestmentioning

confidence: 99%

“…Instead of plotting the whole graph (a), visualizing only part of the graph (b) seems more promising [30,5,16,24]. However, as many real world graphs are scale-free (follow a power law degree distribution [10]), selecting relevant subgraphs to visualize can be challenging [5].…”

Section: Introductionmentioning

confidence: 99%

FACETS: Adaptive Local Exploration of Large Graphs

Pienta¹,

Kahng²,

Lin³

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Visualization is a powerful paradigm for exploratory data analysis. Visualizing large graphs, however, often results in excessive edges crossings and overlapping nodes. We propose a new scalable approach called FACETS that helps users adaptively explore large million-node graphs from a local perspective, guiding them to focus on nodes and neighborhoods that are most subjectively interesting to users. We contribute novel ideas to measure this interestingness in terms of how surprising a neighborhood is given the background distribution, as well as how well it matches what the user has chosen to explore. FACETS uses Jensen-Shannon divergence over information-theoretically optimized histograms to calculate the subjective user interest and surprise scores. Participants in a user study found FACETS easy to use, easy to learn, and exciting to use. Empirical runtime analyses demonstrated FACETS's practical scalability on large real-world graphs with up to 5 million edges, returning results in fewer than 1.5 seconds.

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“…Network visualization research faces the challenge of scalability and interaction design as a major factor to provide a successful visual analysis tool (Pienta, Abello, Kahng & Chau, 2015). Most commonly a series of diagrams gets animated or are shown as small multiples (Archambault et al, 2014;Beck, Burch, Diehl & Weiskopf, 2014 Sense-making as the deliberate effort to understand events (Klein & Phillips, 2007) is mainly influenced by cognitive psychology.…”

Section: Related Workmentioning

confidence: 99%

How Analysts Think: Sense-making Strategies in the Analysis of Temporal Evolution and Criminal Network Structures and Activities

Haider

Seidler

Pohl

et al. 2017

Proceedings of the Human Factors and Ergonomics Society Annual

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Copyright and moral rights to this work are retained by the author and/or other copyright owners unless otherwise stated. The work is supplied on the understanding that any use for commercial gain is strictly forbidden. A copy may be downloaded for personal, non-commercial, research or study without prior permission and without charge.Works, including theses and research projects, may not be reproduced in any format or medium, or extensive quotations taken from them, or their content changed in any way, without first obtaining permission in writing from the copyright holder(s). They may not be sold or exploited commercially in any format or medium without the prior written permission of the copyright holder(s).Full bibliographic details must be given when referring to, or quoting from full items including the author's name, the title of the work, publication details where relevant (place, publisher, date), pagination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award.If you believe that any material held in the repository infringes copyright law, please contact the Repository Team at Middlesex University via the following email address:eprints@mdx.ac.ukThe item will be removed from the repository while any claim is being investigated.See also repository copyright: re-use policy: http://eprints.mdx.ac.uk/policies.html#copy A E Solutions (BI) Ltd, Badsey, UK Analysis of criminal activity based on offenders' social networks is an established procedure in intelligence analysis. The complexity of the data poses an obstacle for analysts to gauge network developments, e.g. detect emerging problems. Visualization is a powerful tool to achieve this, but it is essential to know how the analysts' sense-making strategies can be supported most efficiently. Based on a think aloud study we identified ten cognitive strategies on a general level to be useful for designers. We also provide some examples how these strategies can be supported through appropriate visualizations. How Analysts Think: Sense-making Strategies in the Analysis of Temporal Evolution and Criminal Network Structures and Activities

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Scalable graph exploration and visualization: Sensemaking challenges and opportunities

Cited by 70 publications

References 71 publications

Mining Redescriptions with Siren

Mining Redescriptions with Siren

FACETS: Adaptive Local Exploration of Large Graphs

How Analysts Think: Sense-making Strategies in the Analysis of Temporal Evolution and Criminal Network Structures and Activities

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