Visualisation of structure and processes on temporal networks. (English) Zbl 1540.90047
Holme, Petter (ed.) et al., Temporal network theory. Cham: Springer. Comput. Soc. Sci., 83-105 (2023).
Summary: The temporal dimension increases the complexity of network models but also provides more detailed information about the sequence of connections between nodes allowing a more detailed mapping of processes taking place on the network. The visualisation of such evolving structures thus permits faster identification of non-trivial activity patterns and provides insights about the mechanisms driving the dynamics on and of networks. In this chapter, we introduce key concepts and discuss visualisation methods of temporal networks based on 2D layouts where nodes correspond to horizontal lines with circles to represent active nodes and vertical edges connecting those active nodes at given times. We introduce and discuss algorithms to re-arrange nodes and edges to reduce visual clutter, layouts to highlight node and edge activity, and visualise dynamic processes on temporal networks. We illustrate the methods using real-world temporal network data of face-to-face human contacts and simulated random walk and infection dynamics.
For the entire collection see [Zbl 1531.90014].
For the entire collection see [Zbl 1531.90014].
MSC:
| 90B10 | Deterministic network models in operations research |
Software:
GenLouvainReferences:
| [1] | Archambault, D.; Purchase, HC, Can animation support the visualisation of dynamic graphs?, Inf. Sci., 330, 495-509, 2016 · doi:10.1016/j.ins.2015.04.017 |
| [2] | B. Bach, Unfolding dynamic networks for visual exploration. IEEE Comput. Graph. Appl. 36, 74-82 (2016) |
| [3] | B. Bach, E. Pietriga, J.-D. Fekete, Visualizing dynamic networks with matrixcubes, in Proceedings of the 2014 Annual Conference on Human Factors in Computing Systems (CHI2014) (ACM, 2014), pp. 877-886 |
| [4] | Barabási, A-L, The origin of bursts and heavy tails in human dynamics, Nature, 435, 207-211, 2005 · doi:10.1038/nature03459 |
| [5] | A. Barrat, M. Barthélemy, A. Vespignani, Dynamical Processes on Complex Networks (Cambridge University Press, 2008) · Zbl 1198.90005 |
| [6] | Battista, GD; Eades, P.; Tamassia, R.; Tollis, IG, Algorithms for drawing graphs: an annotated bibliography, Comput. Geom., 4, 5, 235-282, 1994 · Zbl 0804.68001 · doi:10.1016/0925-7721(94)00014-X |
| [7] | F. Beck, M. Burch, S. Diehl, D. Weiskopf, The state of the art in visualizing dynamic graphs, in Eurographics Conference on Visualization (EuroVis) (2014) |
| [8] | M. Behrisch, B. Bach, N. Henry Riche, T. Schreck, J.-D. Fekete, Matrix reordering methods for table and network visualization, in Computer Graphics Forum, vol. 35 (Wiley Online Library, 2016), pp. 693-716 |
| [9] | S. Card, J. Mackinlay, B. Shneiderman, Readings in Information Visualization: Using Vision to Think (Morgan Kaufmann, 1999) |
| [10] | B. Cornelissen, D. Holten, A. Zaidman, L. Moonen, J.J. van Wijk, A. van Deursen, Understanding execution traces using massive sequence and circular bundle views, in ICPC (IEEE Computer Society, 2007), pp. 49-58 |
| [11] | L. da Fontoura Costa, O.N.O. Jr., G. Travieso, F.A. Rodrigues, P.R.V. Boas, L. Antiqueira, M.P. Viana, L.E.C. Rocha, Analyzing and modeling real-world phenomena with complex networks: a survey of applications. Adv. Phys. 60(3), 329-412 (2011) |
| [12] | Ellis, G.; Dix, A., A taxonomy of clutter reduction for information visualisation, IEEE Trans. Vis. Comput. Graph., 13, 6, 1216-1223, 2007 · doi:10.1109/TVCG.2007.70535 |
| [13] | Gleicher, M.; Albers, D.; Walker, R.; Jusufi, I.; Hansen, CD; Roberts, JC, Visual comparison for information visualization, Inf. Vis., 10, 4, 289-309, 2011 · doi:10.1177/1473871611416549 |
| [14] | Holme, P.; Saramäki, J., Temporal networks, Phys. Rep., 519, 97-125, 2012 · doi:10.1016/j.physrep.2012.03.001 |
| [15] | P. Holme F. Liljeros, Birth and death of links control disease spreading in empirical contact networks. Sci. Rep. 4(4999) (2014) |
| [16] | Huang, W.; Eadesband, P.; Hong, S-H, Measuring effectiveness of graph visualizations: A cognitive load perspective, Inf. Vis., 8, 3, 139-152, 2009 · doi:10.1057/ivs.2009.10 |
| [17] | Isella, L.; Stehlé, J.; Barrat, A.; Cattuto, C.; Pinton, J-F; den Broeck, WV, What’s in a crowd? analysis of face-to-face behavioral networks, J. Theor. Biol., 271, 166-180, 2011 · Zbl 1405.92255 · doi:10.1016/j.jtbi.2010.11.033 |
| [18] | Jerding, DF; Stasko, JT, The information mural: a technique for displaying and navigating large information spaces, IEEE Trans. Vis. Comput. Graph., 4, 3, 257-271, 1998 · doi:10.1109/2945.722299 |
| [19] | M. Karsai, H.-H. Jo, K. Kaski, Bursty Human Dynamics (Springer, 2018) |
| [20] | Keim, D., Visual exploration of large data sets, Commun. ACM, 44, 8, 38-44, 2001 · doi:10.1145/381641.381656 |
| [21] | M. Lima, Visual Complexity Mapping Patterns of Information. (Princeton Architectural Press, 2011) |
| [22] | C.D.G. Linhares, J.R. Ponciano, F.S.F. Pereira, L.E.C. Rocha, J.G.S. Paiva, B.A.N. Travençolo, A scalable node ordering strategy based on community structure for enhanced temporal network visualization, unpublished (2019) |
| [23] | C.D.G. Linhares, B.A.N. Travençolo, J.G.S. Paiva, L.E.C. Rocha, DyNetVis: a system for visualization of dynamic networks, in Proceedings of the Symposium on Applied Computing, SAC ’17, (Marrakech, Morocco) (ACM, 2017), pp. 187-194 |
| [24] | R. Mastrandrea, J. Fournet, A. Barrat, Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys. PLOS ONE 10(9) (2015) |
| [25] | N. Masuda, R. Lambiotte, A Guide to Temporal Networks (World Scientific, 2016) · Zbl 1376.60001 |
| [26] | P. Mi, M. Sun, M. Masiane, Y. Cao, C. North, Interactive graph layout of a million nodes. Informatics 3, 23, 12/2016 (2016) |
| [27] | R.G. Miller, Survival Analysis (Wiley, 1997) |
| [28] | J.L. Moreno, Who Shall Survive? A New Approach to The Problem of Human Interrelations (Beacon House Lima, 1934) |
| [29] | Mucha, PJ; Richardson, T.; Macon, K.; Porter, MA; Onnela, J-P, Community structure in time-dependent, multiscale, and multiplex networks, Science, 328, 5980, 876-878, 2010 · Zbl 1226.91056 · doi:10.1126/science.1184819 |
| [30] | M. Newman, Networks: An Introduction (OUP Oxford, 2010) · Zbl 1195.94003 |
| [31] | Ribeiro, B.; Perra, N.; Baronchelli, A., Quantifying the effect of temporal resolution on time-varying networks, Sci. Rep., 3, 3006, 2013 · doi:10.1038/srep03006 |
| [32] | Rocha, LEC, Dynamics of air transport networks: a review from a complex systems perspective, Chin. J. Aeronaut., 30, 469-478, 2017 · doi:10.1016/j.cja.2016.12.029 |
| [33] | Rocha, LEC; Blondel, VD, Bursts of vertex activation and epidemics in evolving networks, PLOS Comput. Biol., 9, 2013 · doi:10.1371/journal.pcbi.1002974 |
| [34] | Rocha, LEC; Masuda, N., Random walk centrality for temporal networks, New J. Phys., 16, 2014 · Zbl 1451.60083 · doi:10.1088/1367-2630/16/6/063023 |
| [35] | Rocha, LEC; Masuda, N.; Holme, P., Sampling of temporal networks: methods and biases, Phys. Rev. E, 96, 5, 2017 · doi:10.1103/PhysRevE.96.052302 |
| [36] | Rossetti, G.; Cazabet, R., Community discovery in dynamic networks: A survey, ACM Comput. Surv., 51, 2, 35, 2018 |
| [37] | Rosvall, M.; Bergstrom, CT, Mapping change in large networks, PLoS ONE, 5, 1, 2010 · doi:10.1371/journal.pone.0008694 |
| [38] | T. Sales, Llull as computer scientist, or why llull was one of us, in Ramon Llull: From the Ars Magna to artificial intelligence, ed. by C. Sierra, A. Fidora, Chap. 2 (Artificial Intelligence Research Institute, Barcelona, Spain, 2011), pp. 25-38 |
| [39] | P.J. Sazama, An overview of visualizing dynamic graphs, Unpublished (2015) |
| [40] | Scholtes, I.; Wider, N.; Pfitzner, R.; Garas, A.; Tessone, CJ; Schweitzer, F., Causality-driven slow-down and speed-up of diffusion in non-markovian temporal networks, Nat. Commun., 5, 5024, 2014 · doi:10.1038/ncomms6024 |
| [41] | Six, JM; Tollis, IG, A framework and algorithms for circular drawings of graphs, J. Discret. Alg., 4, 25-50, 2006 · Zbl 1128.68078 · doi:10.1016/j.jda.2005.01.009 |
| [42] | Starnini, M.; Baronchelli, A.; Barrat, A.; Pastor-Satorras, R., Random walks on temporal networks, Phys. Rev. E, 85, 2012 · doi:10.1103/PhysRevE.85.056115 |
| [43] | R. Tamassia, Handbook of Graph Drawing and Visualization (Chapman and Hall/CRC, 2013) |
| [44] | van den Elzen, S.; Holten, D.; Blaas, J.; van Wijk, JJ, Dynamic network visualization with extended massive sequence views, IEEE Trans. Vis. Comput. Graph., 20, 8, 1087-1099, 2014 · doi:10.1109/TVCG.2013.263 |
| [45] | C. Ware, Information Visualization: Perception for Design, vol. 3 (Morgan Kaufmann Publishers Inc, 2013) |
| [46] | C. Wilke, Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures (O’Reilly, 2019) |
| [47] | Zhao, Y.; She, Y.; Chen, W.; Lu, Y.; Xia, J.; Chen, W.; Liu, J.; Zhou, F., Eod edge sampling for visualizing dynamic network via massive sequence view, IEEE Access, 6, 53006-53018, 2018 · doi:10.1109/ACCESS.2018.2870684 |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
