A Visual-analytic Toolkit for Dynamic Interaction Graphs. Yang, X., Asur, S., Parthasarathy, S., & Mehta, S. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, of KDD '08, pages 1016--1024, New York, NY, USA, 2008. ACM. ![A Visual-analytic Toolkit for Dynamic Interaction Graphs [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In this article we describe a visual-analytic tool for the interrogation of evolving interaction network data such as those found in social, bibliometric, WWW and biological applications. The tool we have developed incorporates common visualization paradigms such as zooming, coarsening and filtering while naturally integrating information extracted by a previously described event-driven framework for characterizing the evolution of such networks. The visual front-end provides features that are specifically useful in the analysis of interaction networks, capturing the dynamic nature of both individual entities as well as interactions among them. The tool provides the user with the option of selecting multiple views, designed to capture different aspects of the evolving graph from the perspective of a node, a community or a subset of nodes of interest. Standard visual templates and cues are used to highlight critical changes that have occurred during the evolution of the network. A key challenge we address in this work is that of scalability - handling large graphs both in terms of the efficiency of the back-end, and in terms of the efficiency of the visual layout and rendering. Two case studies based on bibliometric and Wikipedia data are presented to demonstrate the utility of the toolkit for visual knowledge discovery.
@inproceedings{yang_visual-analytic_2008,
address = {New York, NY, USA},
series = {{KDD} '08},
title = {A {Visual}-analytic {Toolkit} for {Dynamic} {Interaction} {Graphs}},
isbn = {978-1-60558-193-4},
url = {http://doi.acm.org/10.1145/1401890.1402011},
doi = {10.1145/1401890.1402011},
abstract = {In this article we describe a visual-analytic tool for the interrogation of evolving interaction network data such as those found in social, bibliometric, WWW and biological applications. The tool we have developed incorporates common visualization paradigms such as zooming, coarsening and filtering while naturally integrating information extracted by a previously described event-driven framework for characterizing the evolution of such networks. The visual front-end provides features that are specifically useful in the analysis of interaction networks, capturing the dynamic nature of both individual entities as well as interactions among them. The tool provides the user with the option of selecting multiple views, designed to capture different aspects of the evolving graph from the perspective of a node, a community or a subset of nodes of interest. Standard visual templates and cues are used to highlight critical changes that have occurred during the evolution of the network. A key challenge we address in this work is that of scalability - handling large graphs both in terms of the efficiency of the back-end, and in terms of the efficiency of the visual layout and rendering. Two case studies based on bibliometric and Wikipedia data are presented to demonstrate the utility of the toolkit for visual knowledge discovery.},
urldate = {2015-12-18TZ},
booktitle = {Proceedings of the 14th {ACM} {SIGKDD} {International} {Conference} on {Knowledge} {Discovery} and {Data} {Mining}},
publisher = {ACM},
author = {Yang, Xintian and Asur, Sitaram and Parthasarathy, Srinivasan and Mehta, Sameep},
year = {2008},
keywords = {Visual analytics, dynamic interaction networks, graph visualization},
pages = {1016--1024}
}
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