Tools and ApplicationsNo Access

EgoVis: A Visual Analysis System for Social Networks Based on Egocentric Research

Key Laboratory of Knowledge Engineering with Big Data, (Hefei University of Technology), Ministry of Education, P. R. China

School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, P. R. China

E-mail Address: luqiang@hfut.edu.cn

Search for more papers by this author

Jing Huang

School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, P. R. China

E-mail Address: hjing410@163.com

Search for more papers by this author

Yifan Ge

School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, P. R. China

E-mail Address: hfgydxgyf@163.com

Search for more papers by this author

Dajiu Wen

School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, P. R. China

E-mail Address: wen18856331950@126.com

Search for more papers by this author

Bin Chen

School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, P. R. China

E-mail Address: 17356511448@163.com

Search for more papers by this author

, and

Ye Yu

School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, P. R. China

E-mail Address: yuye@hfut.edu.cn

Search for more papers by this author

https://doi.org/10.1142/S0218843019300031Cited by:6 (Source: Crossref)

This article is part of the issue:

Special Issue on Collaborative Computing and Crowd Intelligence
Guest Editors: Yichuan Jiang, Tun Lu, Donghui Lin, Yifeng Zeng and Ting Zhu

Abstract

The development of crowd intelligence makes the structure of social network more complex and changeable. Research on social network should be more in-depth and focus on the changes of structure. Ego-network, which represents the relationship between specific individual and the related people, is a hot issue among the research of dynamic social network. The evolution of ego-network is highly dynamic and pluralistic, it is hard to capture its evolutionary pattern over time. To help users analyze the individual characteristics and hidden patterns in multivariate ego-network, we present EgoVis, an interactive visual analysis system for exploring and analyzing complex structural relationships in dynamic network. Based on the task requirements of network evolution analysis, we propose a task taxonomy which is suitable for ego-network research and analysis, design novel visual fonts, and analyze the evolution of dynamic ego-network relations from the three dimensions: overview, subgroup, and detail-ego. Finally, the validity and practicability of EgoVis are verified on DBLP citation network dataset.

Keywords:

References

1. N. W. Kim et al., Visualizing nonlinear narratives with story curves, IEEE Trans. Vis. Comp. Graphics 24(1) (2017) 595–604. Web of Science, Google Scholar
2. S. P. Borgatti, C. Jones and M. G. Everett , Network measures of social capital, Connections 21(2) (1998) 27–36. Google Scholar
3. A. J. O’Malley et al., Egocentric social network structure, health, and pro-social behaviors in a national panel study of Americans, PLoS One 7(5) (2012) e36250. Web of Science, Google Scholar
4. S. Gao et al., A method to review expert recommendation using topic relevance and expert relationship, Int. J. Coop. Inf. Syst. 27(1) (2018) 1741004. Link, Web of Science, Google Scholar
5. V. Arnaboldi et al., Towards a characterization of egocentric networks in online social networks, On the Move to Meaningful Internet Systems (Springer, Berlin, 2011), pp. 524–533. Google Scholar
6. B. Bach, E. Pietriga and J. D. Fekete , GraphDiaries: Animated transitions and temporal navigation for dynamic networks, IEEE Trans. Vis. Comput. Graphics 20(5) (2014) 740–754. Web of Science, Google Scholar
7. B. Bach, E. Pietriga and J. D. Fekete , Visualizing dynamic networks with matrix cubes, in Proc. SIGCHI Conf. Human Factors in Computing Systems (ACM, 2014), pp. 877–886. Google Scholar
8. S. V. Elzen et al., Dynamic network visualization withextended massive sequence views, IEEE Trans. Vis. Comput. Graphics 20(8) (2014) 1087–1099. Web of Science, Google Scholar
9. M. Tulin , Perceived group cohesion versus actual social structure: A study using social network analysis of egocentric Facebook networks, Soc. Sci. Res. 2018 (2018) 74. Google Scholar
10. M. Saqr and A. Alamro , The role of social network analysis as a learning analytics tool in online problem based learning, BMC Med. Edu. 19(1) (2019) 160. Web of Science, Google Scholar
11. M. S. Patterson and P. Goodson , Social network analysis for assessing college-aged adults’ health: A systematic review, J. Am. Coll. Health 67(1) (2019) 59–67. Web of Science, Google Scholar
12. B. Cao et al., MSM behavior disclosure networks and HIV testing: An egocentric network analysis among MSM in China, AIDS and Behav. 23(5) (2019) 1368–1374. Web of Science, Google Scholar
13. J. Yang et al., The association between the characteristics of egocentric network and HIV prevalence among men who have sex with men, Zhonghua yu fang yi xue za zhi [Chin. J. Prev. Med.] 52(12) (2018) 1215–1219. Google Scholar
14. C. Prell , Social Network Analysis: History, Theory & Methodology (Sage, 2012). Google Scholar
15. E. Bellotti , What are friends for? Elective communities of single people, Social Net. 30(4) (2008) 318–329. Web of Science, Google Scholar
16. D. Fisher , Using egocentric networks to understand communication, IEEE Internet Comput. 9(5) (2005) 20–28. Web of Science, Google Scholar
17. A. Guazzini, A. Valerio and A. Passarella , Egocentric online social networks: Analysis of key features and prediction of tie strength in Facebook, Comput. Commun. 36(10–11) (2013) 1130–1144. Web of Science, Google Scholar
18. S. G. B. Roberts et al., Exploring variation in active network size: Constraints and ego characteristics, Social Networks 31(2) (2009) 138–146. Web of Science, Google Scholar
19. M. J. Lubbers et al., Longitudinal analysis of personal networks. The case of Argentinean migrants in Spain, Social Net. 32(1) (2010) 91–104. Web of Science, Google Scholar
20. M. Harrigan, P. Cunningham and L. Coyle , Using storm to perform dynamic egocentric network motif analysis, 2012 IEEE 12th Int. Conf. Data Mining Workshops (IEEE, 2012), pp. 408–415. Google Scholar
21. S. Bano et al., Multimodal egocentric analysis of focused interactions, IEEE Access 16 (2018) 37493–37505. Web of Science, Google Scholar
22. F. Beck et al., A taxonomy and survey of dynamic graph visualization, Comput. Graphics Forum 36(1) (2017) 133–159. Web of Science, Google Scholar
23. Z. Liu et al., A survey of dynamic network visualization and visual analysis, J. Comput. Aided Des. Comp. Graphics (2016). Google Scholar
24. P. Eades and M. L. Huang , Navigating clustered graphs using force-directed methods, J. Graph Algorithms Appl. 2 (2004) 191–215. Link, Google Scholar
25. F. Beck et al., The state of the art in visualizing dynamic graphs, in Europhysics Conf. Visualization (STARs) (2014). Google Scholar
26. D. L. Arendt and L. M. Blaha, SVEN: Informative visual representation of complex dynamic structure, arXiv:1412.6706. Google Scholar
27. Q. He et al., Revoler: Visual analysis of relation evolution in temporal data, J. Comput. Inf. Syst. (2015). Google Scholar
28. M. Ogawa and K. L. Ma , Software evolution storylines, ACM Symp. Software Visualization (2010), pp. 35–42. Google Scholar
29. Y. Tanahashi, C. H. Hsueh and K. L. Ma , An efficient framework for generating storyline visualizations from streaming data, IEEE Trans. Vis. Comput. Graphics 21(6) (2015) 730–742. Web of Science, Google Scholar
30. C. Bryan, K. L. Ma and J. Woodring , Temporal summary images: An approach to narrative visualization via interactive annotation generation and placement, IEEE Trans. Vis. Comput. Graph 23(1) (2017) 511–520. Web of Science, Google Scholar
31. Q. Lu et al., An effective demonstration for group collaboration based on storyline visualization technology, 2014 IEEE 18th Int. Conf. Computer Supported Cooperative Work in Design (CSCWD) (IEEE, 2014). Google Scholar
32. L. Qiang, C. Bingjie and Z. Haibo , Storytelling by the StoryCake visualization, Visual Comput. (2017). Web of Science, Google Scholar
33. F. Beck et al., Rapid serial visual presentation in dynamic graph visualization, IEEE Symp. Visual Languages & Human-Centric Computing (IEEE Computer Society, 2012). Google Scholar
34. A. Sallaberry, C. Muelder and K. L. Ma , Clustering, visualizing, and navigating for large dynamic graphs, Int. Symp. Graph Drawing (Springer, Berlin, Heidelberg, 2012). Google Scholar
35. S. Rufiange and M. J. McGuffin , Diffani: Visualizing dynamic graphs with a hybrid of difference maps and animation, IEEE Trans. Vis. Comput. Graphics 19(20) (2013) 2556–2565. Web of Science, Google Scholar
36. S. Hadlak, H. Schumann and H. J. Schulz , A survey of multi-faceted graph visualization, Eurographics Conf. Visualization (EuroVis), Vol. 33, Cagliary, Italy (The Eurographics Association, 2015), pp. 1–20. Google Scholar
37. N. Q. Liu et al., EgoNetCloud: Event-based egocentric dynamic network visualization, 2015 IEEE Conf. Visual Analytics Science and Technology (VAST), (IEEE Computer Society, 2015). Google Scholar
38. T. L. Fung and K. L. Ma , Visual characterization of personal bibliographic data using a botanical tree design, in Electronic Proc. IEEE VIS 2015 Workshop on Personal Visualization: Exploring Data in Everyday Life (2015). Google Scholar
39. R. Gove et al., NetVisia: Heat map & matrix visualization of dynamic social network statistics & content, IEEE Third Int. Conf. Privacy (IEEE, 2012). Google Scholar
40. C. Wang et al., 1.5 D Egocentric dynamic network visualization, IEEE Trans. Vis. Comput. Graphics 21(5) (2014) 624–637. Web of Science, Google Scholar
41. F. Reitz, A framework for an ego-centered and time-aware visualization of relations in arbitrary data repositories, arXiv:1009.5183. Google Scholar
42. Y. Wu et al., EgoSlider: Visual analysis of egocentric network evolution, IEEE Trans. Vis. Comput. Graphics 22(1) (2016) 260–269. Web of Science, Google Scholar
43. Q. He et al., MENA: Visual analysis of multivariate egocentric network evolution, Int. Conf. Virtual Reality & Visualization (IEEE, 2017). Google Scholar
44. J. W. Ahn, C. Plaisant and B. Shneiderman , A task taxonomy for network evolution analysis, IEEE Trans. Vis. Comput. Graphics 20(3) (2014) 365–376. Web of Science, Google Scholar
45. R. Gove, A Random Sampling O (n) Force-calculation Algorithm for Graph Layouts (2019). Google Scholar
46. V. D. Blondel and J. L. Guillaume , Fast unfolding of communities in large networks, J. Stat. Mech. Theory Exp. 2008(10) (2008). Web of Science, Google Scholar
47. J. Tang et al., ArnetMiner: Extraction and mining of academic social networks, Proc. ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (ACM, 2008), pp. 990–998. Google Scholar