Calliope-Net: Automatic Generation of Graph Data Facts via Annotated Node-Link Diagrams
Authors: 
Qing Chen, Nan Chen, Wei Shuai, Guande Wu, Zhe Xu, Hanghang Tong, Nan CaoAuthors Info & Claims
Pages 562 - 572
Abstract
Graph or network data are widely studied in both data mining and visualization communities to review the relationship among different entities and groups. The data facts derived from graph visual analysis are important to help understand the social structures of complex data, especially for data journalism. However, it is challenging for data journalists to discover graph data facts and manually organize correlated facts around a meaningful topic due to the complexity of graph data and the difficulty to interpret graph narratives. Therefore, we present an automatic graph facts generation system, Calliope-Net, which consists of a fact discovery module, a fact organization module, and a visualization module. It creates annotated node-link diagrams with facts automatically discovered and organized from network data. A novel layout algorithm is designed to present meaningful and visually appealing annotated graphs. We evaluate the proposed system with two case studies and an in-lab user study. The results show that Calliope-Net can benefit users in discovering and understanding graph data facts with visually pleasing annotated visualizations.
References
[1]
J.-W. Ahn, C. Plaisant, and B. Shneiderman. A task taxonomy for network evolution analysis. IEEE Transactions on Visualization and Computer Graphics, 20 (3): pp. 365–376, 2013.
[2]
G. Aisch and K. Yourish. Connecting the dots behind the 2016 presidential candidates. [Online]. Available: https://www.nytimes.com/interactive/2015/5\07/17/us/elections/2t\l16-presidential-campaigns-staff-connections-clinton-bush-cruz-paul-rubio-walker.html, June 2011. Accessed: March 29, 2021.
[3]
B. Bach, N. Kerracher, K. W. Hall, S. Carpendale, J. Kennedy, and N. Henry Riche. Telling stories about dynamic networks with graph comics. In Proceedings of the ACM Conference on Human Factors in Computing Systems, pp. 3670–3682, 2016.
[4]
M. Bastian, S. Heymann, and M. Jacomy. Gephi: An open source software for exploring and manipulating networks. in Proceedings of the International AAAI Conference on Web and Social Media, 3 (1), Mar. 2009.
[5]
S. L. F. Beck. Vis author profiles: Interactive descriptions of publication records combining text and visualization. IEEE Transactions on Visualization and Computer Graphics, 25 (1): pp. 152–161, 2018.
[6]
C. Bennett, J. Ryall, L. Spalteholz, and A. A. Gooch. The aesthetics of graph visualization. In Proceedings of the Computational Aesthetics in Graphics, Visualization, and Imaging, pp. 57–64, 2007.
[7]
P. Bonacich. Power and centrality: A family of measures. American journal of sociology. 92 (5): pp. 1170–1182, 1987.
[8]
L. Bounegru, T. Venturini, J. Gray, and M. Jacomy. Narrating networks: Exploring the affordances of networks as storytelling devices in journalism. Digital Journalism, 5 (6): pp. 699–730, 2017.
[9]
R. Brath and M. Matusiak. Automated annotations. In An IEEE VIS workshop on visualization for communication (VisComm), 2018.
[10]
C. Bryan, K.-L. Ma, and J. Woodring. Temporal summary images: An approach to narrative visualization via interactive annotation generation and placement. IEEE Transactions on Visualization and Computer Graphics, 23 (1): pp. 511–520, 2016.
[11]
E. M. Campbell, H. Jia, A. Shankar, D. Hanson, W. Luo, S. Masciotra, S. M. Owen, A. M. Oster, R. R. Galang, M. W. Spiller et al., Detailed transmission network analysis of a large opiate-driven outbreak of hiv infection in the united states. The Journal of Infectious Diseases, 216 (9): pp. 1053–1062, 2017.
[12]
C. Chabot, C. Stolte, and P. Hanrahan. Tableau software. Tableau Software, 6, 2003.
[13]
Q. Chen, S. Cao, J. Wang, and N. Cao. How does automation shape the process of narrative visualization: A survey of tools. IEEE Transactions on Visualization and Computer Graphics, 2023.
[14]
Z. Chen, Y. Wang, Q. Wang, Y. Wang, and H. Qu. Towards automated infographic design: Deep learning-based auto-extraction of extensible timeline. IEEE Transactions on Visualization and Computer Graphics, 26 (1): pp. 917–926, 2020.
[15]
E. K. Choe, B. Lee et al., Characterizing visualization insights from quantified selfers' personal data presentations. IEEE computer graphics and applications, 35 (4): pp. 28–37, 2015.
[16]
A. Clauset, M. E. Newman, and C. Moore. Finding community structure in very large networks. Phys. Rev. E, 70: p. 066111, Dec 2004.
[17]
S. Cohen, L. Spirito, and A. Crites. The bush money machine. [Online]. Available: http://www.washingtonpost.com/wp-srv/politics/pioneers/network_graphic.pdf, May 2004. Accessed: March 29, 2021.
[18]
Ç. Demiralp, P. J. Haas, S. Parthasarathy, and T. Pedapati. Foresight: Recommending visual insights. arXiv preprint arXiv:, 2017.
[19]
M. Deodhar, X. Ma, Y. Cai, A. Koes, A. Beutel, and J. Chen. A human-ml collaboration framework for improving video content reviews. arXiv preprint arXiv:, 2022.
[20]
R. Ding, S. Han, Y. Xu, H. Zhang, and D. Zhang. Quickinsights: Quick and automatic discovery of insights from multi-dimensional data. In Proceedings of the ACM International Conference on Management of Data, pp. 317–332, Jun 2019.
[21]
T. Eiter and H. Mannila. Computing discrete fréchet distance. Technical report, Citeseer, 1994.
[22]
J. Ellson, E. Gansner, L. Koutsofios, S. C. North, and G. Woodhull. Graphviz—open source graph drawing tools. In International Symposium on Graph Drawing, pp. 483–484. Springer, Feb 2001.
[23]
G. W. Flake, S. Lawrence, and C. L. Giles. Efficient identification of web communities. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 150–160, Aug 2000.
[24]
S. Fortunato. Community detection in graphs. Physics Reports, 486 (3–5): pp. 75–174, 2010.
[25]
L. Freeman. The development of social network analysis. A Study in the Sociology of Science, 1 (687): pp. 159–167, Jan 2004.
[26]
E. R. Gansner, Y. Koren, and S. North. Graph drawing by stress majorization. In International Symposium on Graph Drawing, pp. 239–250. Springer, 2004.
[27]
Z. W. Geem, J. H. Kim, and G. V. Loganathan. A new heuristic optimization algorithm: Harmony search. Simulation, 76 (2): pp. 60–68, 2001.
[28]
J. C. Gower. Generalized procrustes analysis. Psychometrika, 40 (1): pp. 33–51, 1975.
[29]
D. Hansen, B. Shneiderman, and M. A. Smith. Analyzing social media networks with NodeXL: Insights from a connected world. International Journal of Human-Computer Interaction, 27 (4): pp. 405–408, Feb 2010.
[30]
J. Hart. Storycraft: The complete guide to writing narrative nonfiction. University of Chicago Press, 2021.
[31]
I. Herman, G. Melançon, and M. S. Marshall. Graph visualization and navigation in information visualization: A survey. IEEE Transactions on Visualization and Computer Graphics, 6 (1): pp. 24–43, 2000.
[32]
W. Huang, P. Eades, and S.-H. Hong. in Layout effects: Comparison of sociogram drawing conventions. School of Information Technologies, University of Sydney Darlington, England, Jan 2005.
[33]
J. Hullman, N. Diakopoulos, and E. Adar. Contextifier: automatic generation of annotated stock visualizations. In Proceedings of the ACM Conference on Human Factors in Computing Systems, pp. 2707–2716, 2013.
[34]
J. Hullman, S. Drucker, N. H. Riche, B. Lee, D. Fisher, and E. Adar. A deeper understanding of sequence in narrative visualization. IEEE Transactions on Visualization and Computer Graphics, 19 (12): pp. 2406–2415, 2013.
[35]
J. Ilbo. Social network analysis of high-ranking officials in s. korean government. [Online]. Available: https://www.ire.org/product/story-25691/, 2012. Accessed: March 29, 2021.
[36]
D. Kaneider, T. Seifried, and M. Haller. Automatic annotation placement for interactive maps. In Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces, pp. 61–70. Association for Computing Machinery, 2013.
[37]
N. W. Kim, N. Henry Riche, B. Bach, G. Xu, M. Brehmer, K. Hinckley, M. Pahud, H. Xia, M. J. McGuffin, and H. Pfister. Datatoon: Drawing dynamic network comics with pen+touch interaction. In Proceedings of the ACM Conference on Human Factors in Computing Systems, pp. 1–12, 2019.
[38]
C. N. Knaflic. Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons, 2015.
[39]
D. E. Knuth. The stanford graphbase: a platform for combinatorial algorithms. In SODA, vol. 93, pp. 41–43, 1993.
[40]
R. Kosara and J. Mackinlay. Storytelling: The next step for visualization. Computer, 46 (5): pp. 44–50, 2013.
[41]
S. Latif, K. Su, and F. Beck. Authoring combined textual and visual descriptions of graph data. In EuroVis (Short Papers), pp. 115–119, 2019.
[42]
S. Latif, Z. Zhou, Y. Kim, F. Beck, and N. W. Kim. Kori: Interactive synthesis of text and charts in data documents. IEEE Transactions on Visualization and Computer Graphics, 28 (1): pp. 184–194, 2021.
[43]
P.-M. Law, A. Endert, and J. Stasko. Characterizing automated data insights. In 2020 IEEE Visualization Conference (VIS), pp. 171–175. IEEE, 2020.
[44]
J. Lazar, J. H. Feng, and H. Hochheiser. Research methods in human-computer interaction. Morgan Kaufmann, 2017.
[45]
B. Lee, C. Plaisant, C. S. Parr, J.-D. Fekete, and N. Henry. Task taxonomy for graph visualization. In Proceedings of the 2006 AVI workshop on BEyond Time and Errors Novel Evaluation Methods for Information Visualization - BELIV '06, p. 1. ACM Press, 2006.
[46]
B. Lee, N. H. Riche, P. Isenberg, and S. Carpendale. More than telling a story: Transforming data into visually shared stories. IEEE computer graphics and applications, 35 (5): pp. 84–90, 2015.
[47]
D. J.-L. Lee, H. Dev, H. Hu, H. Elmeleegy, and A. Parameswaran. Avoiding drill-down fallacies with VisPilot: Assisted exploration of data subsets. In Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 186–196, march 2019.
[48]
M. Lu, C. Wang, J. Lanir, N. Zhao, H. Pfister, D. Cohen-Or, and H. Huang. Exploring visual information flows in infographics. In Proceedings of the ACM Conference on Human Factors in Computing Systems, pp. 1–12, 2020.
[49]
Y. Luo, C. Chai, X. Qin, N. Tang, and G. Li. Interactive cleaning for progressive visualization through composite questions. In 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 733–744. IEEE, 2020.
[50]
R. Mafrur, M. A. Sharaf, and H. A. Khan. Dive: Diversifying view recommendation for visual data exploration. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1123–1132, 2018.
[51]
B. Marsh and K. Zernike. Chris christie and the lane closings: A spectator's guide. [Online]. Available: https://www.nytimes.com/interactive/2015/04/08/nyregion/chris-christie-and-bridgegate-guide.html, Apr. 2015. Accessed: March 29, 2021.
[52]
D. F. Nettleton. Data mining of social networks represented as graphs. Computer Science Review, 7: pp. 1–34, 2013.
[53]
C. Nobre, M. Meyer, M. Streit, and A. Lex. The state of the art in visualizing multivariate networks. Computer Graphics Forum, 38 (3): pp. 807–832, 2019.
[54]
OCCRP. The proxy platform. [Online]. Available: https://www.reportingproject.net/proxy/en/, 2011. Accessed: March 29, 2021.
[55]
K.-L. M. Oh-Hyun Kwon, Tarik Crnovrsanin. What would a graph look like in this layout? a machine learning approach to large graph visualization. IEEE Transactions on Visualization and Computer Graphics, 24: pp. 478–488, 2017.
[56]
M. Pohl, M. Schmitt, and S. Diehl. Comparing the readability of graph layouts using eyetracking and task-oriented analysis. In Computational Aesthetics in Graphics, Visualization, and Imaging, pp. 49–56, 2009.
[57]
J. Pretorius, H. C. Purchase, and J. T. Stasko. “Tasks for multivariate network analysis”. In Multivariate Network Visualization, pp. 77–95. Springer, 2014.
[58]
H. C. Purchase. Effective information visualisation: a study of graph drawing aesthetics and algorithms. Interacting with Computers, 13 (2): pp. 147–162, 2000.
[59]
D. Ren, M. Brehmer, B. Lee, T. Höllerer, and E. K. Choe. Chartaccent: Annotation for data-driven storytelling. In IEEE Pacific Visualization Symposium (PacificVis), pp. 230–239. IEEE, 2017.
[60]
H. Romat, C. Appert, and E. Pietriga. Expressive authoring of nodelink diagrams with graphies. IEEE Transactions on Visualization and Computer Graphics, 27 (4): pp. 2329–2340, 2019.
[61]
E. Segel and J. Heer. Narrative visualization: Telling stories with data. IEEE Transactions on Visualization and Computer Graphics, 16 (6): pp. 1139–1148, 2010.
[62]
P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B. Schwikowski, and T. Ideker. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research, 13 (11): pp. 2498–2504, 2003.
[63]
D. Shi, X. Xu, F. Sun, Y. Shi, and N. Cao. Calliope: Automatic visual data story generation from a spreadsheet. IEEE Transactions on Visualization and Computer Graphics, 27 (2): pp. 453–463, 2021.
[64]
J. Shi and J. Malik. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (8): pp. 888–905, 2000.
[65]
A. Srinivasan, S. M. Drucker, A. Endert, and J. Stasko. Augmenting visualizations with interactive data facts to facilitate interpretation and communication. IEEE Transactions on Visualization and Computer Graphics, 25 (1): pp. 672–681, 2019.
[66]
Stack Overflow. Stack overflow tag network. [Online]. Available: https://www.kaggle.com/stackoverf1ow/stack-overf1ow-tag-network, 2021. Accessed: March 15, 2021.
[67]
B. Tang, S. Han, M. L. Yiu, R. Ding, and D. Zhang. Extracting top-k insights from multi-dimensional data. In Proceedings of the ACM International Conference on Management of Data, pp. 1509–1524, 2017.
[68]
G. I. Team, R. Procter, F. Vis, and A. Voss. How riot rumours spread on twitter. [Online]. Available: https:I/www.po1itico.eu/interactive/lobbyists-brussels-social-network-meetings-commission-strategy/, Dec. 2011. Accessed: March 29, 2021.
[69]
M. Vartak, S. Rahman, S. Madden, A. Parameswaran, and N. Polyzotis. Seedb: Efficient data-driven visualization recommendations to support visual analytics. In Proceedings of the VLDB Endowment International Conference on Very Large Data Bases, vol. 8, p. 2182. NIH Public Access, 2015.
[70]
J. Von Daniels, O. Marta, M. Klack, S. Wehrmeyer, and S. Jockers. Die ttip-dealer. [Online]. Available: https:llcorrectiv.org/recherchen/ttip/dea1er/. Accessed: March 29, 2021.
[71]
Y. Wang, Z. Sun, H. Zhang, W. Cui, K. Xu, X. Ma, and D. Zhang. DataShot: Automatic generation of fact sheets from tabular data. IEEE Transactions on Visualization and Computer Graphics, 26 (1): pp. 895–905, 2019.
[72]
Y. Wang, Y. Wang, Y. Sun, L. Zhu, K. Lu, C.-W. Fu, M. Sedlmair, O. Deussen, and B. Chen. Revisiting stress majorization as a unified framework for interactive constrained graph visualization. IEEE Transactions on Visualization and Computer Graphics, 24 (1): pp. 489–499, 2017.
[73]
D. J. Watts and S. H. Strogatz. Collective dynamics of ‘small-world’ networks. nature, 393 (6684): pp. 440–442, 1998.
[74]
Wikipedia contributors. Les misérables- Wikipedia, the free encyclopedia. [Online]. Available: https://en.wikipedia.org/w/index.php?title=Les_Mis%C3%A9rab1es&oldid=1006098025, 2021. Accessed: March 31, 2021.
[75]
H.-Y. Wu. Focus+context metro map layout and annotation. In Proceedings of the 32nd Spring Conference on Computer Graphics, pp. 41–47, 2016.
[76]
Yang Chen, Jing Yang, and W. Ribarsky. Toward effective insight management in visual analytics systems. In IEEE Pacific Visualization Symposium (PacificVis), pp. 49–56, Apr. 2009. ISSN:.
[77]
B. Z. Zhang Peng. A classification sampling algorithm over the dynamic streaming graph. in 2022 4th International Conference on Advances in Computer Technology, Information Science and Communications (CTISC), 2022.
[78]
Y. Zhao, H. Jiang, Y. Qin, H. Xie, Y. Wu, S. Liu, Z. Zhou, J. Xia, F. Zhou et al., Preserving minority structures in graph sampling. IEEE Transactions on Visualization and Computer Graphics, 27 (2): pp. 1698–1708, 2020.
Index Terms
- Calliope-Net: Automatic Generation of Graph Data Facts via Annotated Node-Link Diagrams
Index terms have been assigned to the content through auto-classification.
Recommendations
Motion to support rapid interactive queries on node--link diagrams
Many different problems can be represented as graphs displayed in the form of node--link diagrams. However, when a graph is large it becomes visually uninterpretable because of the tangle of links. We describe a set of techniques that use motion in an ...
Visualizing Patterns in Node-link Diagrams
IV '12: Proceedings of the 2012 16th International Conference on Information VisualisationPattern discovery plays an important part in the graph analysis process. Good examples are the detection of communities in social networks or the clustering into pathways of metabolic networks. However, elements may be shared by several clusters, making ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
1077-2626 © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.
Publisher
IEEE Educational Activities Department
United States
Publication History
Published: 24 October 2023
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 12 Dec 2024
Other Metrics
Citations
View Options
View options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in