TY - JOUR
T1 - Eiffel
T2 - Evolutionary Flow Map for Influence Graph Visualization
AU - Huang, Yucheng
AU - Shi, Lei
AU - Su, Yue
AU - Hu, Yifan
AU - Tong, Hanghang
AU - Wang, Chaoli
AU - Yang, Tong
AU - Wang, Deyun
AU - Liang, Shuo
N1 - Funding Information:
This work was supported by NSFC Grants 61772504, 61672061, U.S. NSF Grant IIS-1456763, IIS-1455886, IIS-1651203, IIS-1715385, and DUE-1833129.
Publisher Copyright:
© 1995-2012 IEEE.
PY - 2020/10/1
Y1 - 2020/10/1
N2 - The visualization of evolutionary influence graphs is important for performing many real-life tasks such as citation analysis and social influence analysis. The main challenges include how to summarize large-scale, complex, and time-evolving influence graphs, and how to design effective visual metaphors and dynamic representation methods to illustrate influence patterns over time. In this work, we present Eiffel, an integrated visual analytics system that applies triple summarizations on evolutionary influence graphs in the nodal, relational, and temporal dimensions. In numerical experiments, Eiffel summarization results outperformed those of traditional clustering algorithms with respect to the influence-flow-based objective. Moreover, a flow map representation is proposed and adapted to the case of influence graph summarization, which supports two modes of evolutionary visualization (i.e., flip-book and movie) to expedite the analysis of influence graph dynamics. We conducted two controlled user experiments to evaluate our technique on influence graph summarization and visualization respectively. We also showcased the system in the evolutionary influence analysis of two typical scenarios, the citation influence of scientific papers and the social influence of emerging online events. The evaluation results demonstrate the value of Eiffel in the visual analysis of evolutionary influence graphs.
AB - The visualization of evolutionary influence graphs is important for performing many real-life tasks such as citation analysis and social influence analysis. The main challenges include how to summarize large-scale, complex, and time-evolving influence graphs, and how to design effective visual metaphors and dynamic representation methods to illustrate influence patterns over time. In this work, we present Eiffel, an integrated visual analytics system that applies triple summarizations on evolutionary influence graphs in the nodal, relational, and temporal dimensions. In numerical experiments, Eiffel summarization results outperformed those of traditional clustering algorithms with respect to the influence-flow-based objective. Moreover, a flow map representation is proposed and adapted to the case of influence graph summarization, which supports two modes of evolutionary visualization (i.e., flip-book and movie) to expedite the analysis of influence graph dynamics. We conducted two controlled user experiments to evaluate our technique on influence graph summarization and visualization respectively. We also showcased the system in the evolutionary influence analysis of two typical scenarios, the citation influence of scientific papers and the social influence of emerging online events. The evaluation results demonstrate the value of Eiffel in the visual analysis of evolutionary influence graphs.
KW - Influence graph
KW - citation analysis
KW - dynamic visualization
UR - http://www.scopus.com/inward/record.url?scp=85090252574&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090252574&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2019.2906900
DO - 10.1109/TVCG.2019.2906900
M3 - Article
C2 - 30908230
AN - SCOPUS:85090252574
SN - 1077-2626
VL - 26
SP - 2944
EP - 2960
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 10
M1 - 8673661
ER -