We introduce CommViz, an information visualization tool that enables complex communication networks to be explored, exposing trends and patterns in the data at a glance. We adapt a visualization approach known as hive plots to reflect the semantic structure of the networks, a generalization we call semantic hive plots. The method efficiently organizes and provides insight into complex, high-dimensional communication data such as email and messages on social media. We present the architecture of the CommViz tool and its application to the Enron email corpus as a case study, demonstrating how the structure of the visualization enables investigation of patterns and relationships in a large set of messages. We also provide a user study performed with Amazon Mechanical Turk that shows the value of the tool for certain complex data interrogations and further show how the incorporation of semantic structure on semantic coordinates can also be applied to parallel coordinates visualization. The integration of the social network characteristics with semantic attributes of the underlying data in a single visualization is, to our knowledge, a novel contribution of the work. The tool can be accessed at http://commviz.eng.unimelb.edu.au. Code is available at https://bitbucket.org/readbiomed/commviz. The Enron email corpus is available from http://bailando.sims.berkeley.edu/enron_email.html.
KlimtBYangY . Introducing the Enron corpus. In: First conference on email and anti-spam (CEAS), Mountain View, CA, 30–31 July 2004.
4.
DiesnerJCarleyKM. Communication networks from the Enron email corpus “it’s always about the People. Enron is no different.”Comput Math Organ Theor2005; 11: 201–228.
BleiD. Probabilistic topic models. Comm ACM2012; 55(4): 77–84.
7.
McCallumAWangXCorrada-EmmanuelA. Topic and role discovery in social networks with experiments on Enron and academic email. J Artif Intell Res2007; 30: 249–272.
8.
HeerJBoydD. Vizster: visualizing online social networks. In: IEEE symposium on information visualization, Minneapolis, MN, 23–25 October 2005, pp. 32–39. New York: IEEE.
9.
BianJXieMHudsonTJet al. CollaborationViz: interactive visual exploration of biomedical research collaboration networks. PLoS ONE2014; 9(11): e111928.
10.
GehlenborgNO’DonoghueSIBaligaNSet al. Visualization of omics data for systems biology. Br J Pharmacol2010; 7(Suppl. 3): S56–S68.
11.
HermanIMelanconGMarshallMS. Graph visualization and navigation in information visualization: a survey. IEEE Trans Visual Comput Graph2000; 6(1): 24–43.
12.
LiebermanMDTaheriSGuoHet al. Visual exploration across biomedical databases. IEEE/ACM Trans Comput Biol Bioinform2011; 8(2): 536–550.
13.
HeerJPererA. Orion: a system for modeling, transformation and visualization of multidimensional heterogeneous networks. Inform Visual2012; 13(2): 111–133.
14.
McGuffinMJ. Simple algorithms for network visualization: a tutorial. Tsinghua Sci Technol2012; 17(4): 383–398.
15.
LiuXShenHW. The effects of representation and juxtaposition on graphical perception of matrix visualization. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems, Seoul, Korea, 18–23 April 2015, pp. 269–278. New York: ACM.
16.
BachBPietrigaEFeketeJD. Visualizing dynamic networks with matrix cubes. In: Proceedings of the SIGCHI conference on human factors in computing systems, Toronto, ON, Canada, 26 April–1 May 2014, pp. 877–886. New York: ACM.
17.
EngleSWhalenS. Visualizing distributed memory computations with hive plots. In: Proceedings of the ninth international symposium on visualization for cyber security, Seattle, WA, 15 October 2012, pp. 56–63. New York: ACM.
18.
GuimeràRDanonLDíaz-GuileraAet al. The real communication network behind the formal chart: community structure in organizations. J Econ Behav Organ2006; 61(4): 653–667.
19.
TylerJRWilkinsonDMHubermanBA. Email as spectroscopy: automated discovery of community structure within organizations. In: HuysmanMWengerEWulfV (eds) Communities and technologies. Deventer: Wolters Kluwer, 2003, pp. 81–96.
20.
SimsBHSinitsynNEidenbenzSJ. Hierarchical and matrix structures in a large organizational email network: visualization and modeling approaches. In: MissaouiRSarrI (eds) Social network analysis—community detection and evolution. Berlin; Heidelberg: Springer, 2014, pp. 27–43.
21.
KerrB. Thread arcs: an email thread visualization. In: IEEE symposium on information visualization, Seattle, WA, 19–21 October 2003, pp. 211–218. New York: IEEE.
22.
VenoliaGDNeustaedterC. Understanding sequence and reply relationships within email conversations: a mixed-model visualization. In: Proceedings of the SIGCHI conference on human factors in computing systems, Fort Lauderdale, FL, 5–10 April 2003, pp. 361–368. New York: ACM.
23.
PererASmithMA. Contrasting portraits of email practices: visual approaches to reflection and analysis. In: Proceedings of the working conference on advanced visual interfaces (AVI ’06), Venice, 23–26 May 2006, pp. 389–395. New York: ACM.
24.
FuXHongSHNikolovNSet al. Visualization and analysis of small-world email networks. In: 12th IEEE symposium on information visualization 2006, Baltimore, MD, 29 October–3 November 2006.
25.
SackW. Conversation map: an interface for very large-scale conversations. J Manag Inform Syst2001; 17(3): 73–92.
26.
ViégasFGolderSDonathJ. Visualizing email content: portraying relationships from conversational histories. In: Proceedings of the SIGCHI conference on human factors in computing systems, Montreal, QC, Canada, 22–27 April, 2006, pp. 979–988. New York: ACM.
27.
LiuSZhouMXPanSet al. TIARA: interactive, topic-based visual text summarization and analysis. ACM T Intell Sys Tech2012; 3(2): 25.
28.
ChuangJManningCDHeerJ. Termite: visualization techniques for assessing textual topic models. In: Proceedings of the international working conference on advanced visual interfaces, 2012, pp. 74–77, http://vis.stanford.edu/papers/termite
29.
NewmanDBaldwinTCavedonLet al. Visualizing search results and document collections using topic maps. Web Semant: Sci Serv Agents World Wide Web2010; 8(2–3): 169–175.
30.
GretarssonBO’DonovanJBostandjievSet al. TopicNets: visual analysis of large text corpora with topic modeling. ACM T Intell Sys Tech2012; 3(2): 23.
WattenbergMViégasFB. The Word Tree, an interactive visual concordance. IEEE Trans Visual Comput Graph2008; 14(6): 1221–1228.
33.
HeinrichJWeiskopfD. State of the art of parallel coordinates. In: SbertMSzirmay-KalosL (eds) Eurographics 2013–state of the art reports. Aire-la-Ville: Eurographics Association, 2013, pp. 95–116.
InselbergA. The plane with parallel coordinates. Visual Comput1985; 1(2): 69–91.
36.
JanetzkoHSteinMSachaDet al. Enhancing parallel coordinates: statistical visualizations for analyzing soccer data. In: IS&T electronic imaging conference on visualization and data analysis, 14–18 February 2016, pp. 1–8. San Francisco, CA: The Society for Imaging Science and Technology.
37.
HenryNFeketeJD. MatrixExplorer: a dual-representation system to explore social networks. IEEE Trans Visual Comput Graph2006; 12(5): 677–684.
38.
HenryNFeketeJD. MatLink: enhanced matrix visualization for analyzing social networks. In: BaranauskasCPalanquePAbascalJet al. (eds) Human-computer interaction—INTERACT 2007, vol. 4663. Rio de Janeiro, Brazil: Springer, 2007, pp. 288–302.
39.
HenryNFeketeJDMcGuffinMJ. NodeTrix: a hybrid visualization of social networks. IEEE Trans Visual Comput Graph2007; 13(6): 1302–1309.
40.
ShenyZMazKL. Path visualization for adjacency matrices. In: Proceedings of the 9th joint Eurographics/IEEE VGTC conference on visualization, Norrköping, 23–25 May 2007, pp. 83–90. Aire-la-Ville: Eurographics Association.
41.
WattenbergM. Visual exploration of multivariate graphs. In: Proceedings of the SIGCHI conference on human factors in computing systems, Montreal, QC, Canada, 22–27 April, 2006, pp. 811–819. New York: ACM.
42.
ShneidermanBArisA. Network visualization by semantic substrates. IEEE Trans Visual Comput Graph2006; 12(5): 733–740.
43.
Van Den ElzenSVan WijkJJ. Multivariate network exploration and presentation: from detail to overview via selections and aggregations. IEEE Trans Visual Comput Graph2014; 20(12): 2310–2319.
44.
BraunLVolkeMSchlampJet al. Flow-inspector: a framework for visualizing network flow data using current web technologies. Computing2014; 96(1): 15–26.
WhitingMCookKGrinsteinGet al. Vast challenge 2015: mayhem at dinofun world. In: 2015 IEEE conference on visual analytics science and technology (VAST), Chicago, IL, 25–30 October 2015, pp. 113–118. New York: IEEE.
49.
PlaisantC. The challenge of information visualization evaluation. In: Proceedings of the working conference on advanced visual interfaces, Gallipoli, 25–28 May 2004, pp. 109–116. New York: ACM.
50.
PlaisantCGrosjeanJBedersonB. SpaceTree: supporting exploration in large node link tree, design evolution and empirical evaluation. In: IEEE symposium on information visualization, Boston, MA, 28–29 October 2002, pp. 57–64. New York: IEEE.
51.
KobsaA. An empirical comparison of three commercial information visualization systems. In: Proceedings of the IEEE symposium on information visualization 2001, San Diego, CA, 22–23 October 2001, pp. 123–130. Washington, DC: IEEE Computer Society.
52.
StaskoJCatramboneRGuzdialMet al. An evaluation of space-filling information visualizations for depicting hierarchical structures. Int J Hum: Comput St2000; 53(5): 663–694.
53.
Vande MoereATomitschMWimmerCet al. Evaluating the effect of style in information visualization. IEEE Trans Visual Comput Graph2012; 18(12): 2739–2748.
54.
StaskoJ. Value-driven evaluation of visualizations. In: Proceedings of the fifth workshop on beyond time and errors: novel evaluation methods for visualization, Paris, 10 November 2014, pp. 46–53. New York: ACM.
55.
HeerJBostockM. Crowdsourcing graphical perception: using Mechanical Turk to assess visualization design. In: Proceedings of the SIGCHI conference on human factors in computing systems, Atlanta, GA, 10–15 April 2010, pp. 203–212. New York: ACM.
56.
Abdul-RahmanAProctorKJDuffyBet al. Repeated measures design in crowdsourcing-based experiments for visualization. In: Proceedings of the fifth workshop on beyond time and errors: novel evaluation methods for visualization, Paris, 10 November 2014, pp. 95–102. New York: ACM.
VartakMRahmanSMaddenSet al. SeeDB: efficient data-driven visualization recommendations to support visual analytics. Proceedings VLDB Endowment2015; 8(13): 2182–2193.
59.
HaggertyJLambDTaylorM. Social network visualization for forensic investigation of e-mail. In: Proceedings of the 4th international workshop on digital forensics and incident analysis (WDFIA 2009), Athens, Greece, 25–26 June 2009, pp. 81–92. UK: Plymouth University.
60.
ShannonPMarkielAOzierOet al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res2003; 13: 2498–2504.
LandisJRKochGG. The measurement of observer agreement for categorical data. Biometrics1977; 33(1): 159–174.
63.
JohanssonJForsellC. Evaluation of parallel coordinates: Overview, categorization and guidelines for future research. IEEE T Vis Comput Gr2016; 22(1): 579–588. DOI: 10.1109/TVCG.2015.2466992.
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