Part 1: Network Visualisation Tim Dwyer tim.dwyer@monash.edu - PowerPoint PPT Presentation

Part 1: Network Visualisation Tim Dwyer tim.dwyer@monash.edu ialab.it.monash.edu/~dwyer/ Monash University, Australia October 2019

projects.icij.org/ panama-papers/ power-players

Atlas of economic complexity

Network Earth

Keystone taxa as drivers of microbiome structure and functioning, Banerjee et al. 2018

Yehuda Koren Karsten Klein Bongshin Lee Vahan Yoghourdjian Chunlei Chang Steve Kieffer Google Uni Konstanz Microsoft Research General Assembly Credits Nathalie Henry Riche Sheelagh Carpendale Microsoft Research Simon Fraser Uni Michael Wybrow Benjamin Bach Uni Edinburgh Kun-Ting Chen George Robertson Graeme Gange Peter Stuckey Kim Marriott Yalong Yang, Harvard Microsoft Research

Tim Dwyer, Yehuda Koren, and Kim Marriott. "IPSep-CoLa: An incremental procedure for separation constraint layout of graphs." IEEE Transactions on Visualization and Computer Graphics 12.5 (2006): 821-828.

Stress majorization stress ( X ) x* y* x* y* ( x , y ) *

Constrained stress majorization  Instead of solving unconstrained quadratic forms we solve subject to separation constraints  i.e. Quadratic Programming stress ( X ) x* y* x* y* ( x , y ) *

Gradient projection x 0 -g - α g x 1

Gradient projection x 1 - α g

Gradient projection x 2 β d x 1 d

Gradient projection x*

Tim Dwyer, Yehuda Koren IEEE Symposium on Information Visualization, 2005. INFOVIS 2005, 65-72

Fast node overlap removal T Dwyer, K Marriott, PJ Stuckey International Symposium on Graph Drawing, 153-164, 2005 Separation Constraints x 1 + d ≤ x 2 y 1 + d ≤ y 2 w 2 w 1 h 2 (x 2 ,y 2 ) (x 1 ,y 1 ) ( h 2 +h 3 ) y 3 + ≤ y 2 ( w 1 +w 2 ) 2 x 1 + ≤ x 2 h 3 (x 3 ,y 3 ) 2

IPSep-CoLa Tim Dwyer, Yehuda Koren, and Kim Marriott. “Unix” Graph "IPSep-CoLa: An incremental procedure for data From www.graphviz.org separation constraint layout of graphs." IEEE Transactions on Visualization and Computer Graphics 12.5 (2006): 821-828.

Topology preserving constrained graph layout T Dwyer, K Marriott, M Wybrow International Symposium on Graph Drawing, 230-241, 2008

https://ialab.it.monash.edu/webcola

Tim Dwyer, Network Visualization as a Higher-Order Visual Analysis Tool IEEE computer graphics and applications 36(6), pp. 78-85, 2016.

Dwyer, Tim, Nathalie Henry Riche, Kim Marriott, and Christopher Mears. "Edge compression techniques for visualization of dense directed graphs." IEEE transactions on visualization and computer graphics 19, no. 12 (2013): 2596-2605.

Dwyer, Tim, Nathalie Henry Riche, Kim Marriott, and Christopher Mears. "Edge compression techniques for visualization of dense directed graphs." IEEE transactions on visualization and computer graphics 19, no. 12 (2013): 2596-2605.

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Improved Optimal and Approximate Power Graph Compression for Clearer Visualisation of Dense Graphs T Dwyer, C Mears, K Morgan, T Niven, K Marriott, M Wallace Pacific Visualization Symposium (PacificVis), 2014 IEEE, 105-112

Yoghourdjian, V., Dwyer, T ., Gange, G., Kieffer, S., Klein, K., & Marriott, K. High-quality ultra-compact grid layout of grouped networks. IEEE Transactions on Visualization and Computer Graphics, 22(1), 339-348. 2015

Kieffer, S., Dwyer, T., Marriott, K., & Wybrow, M. Hola: Human-like orthogonal network layout. IEEE transactions on visualization and computer graphics , 22 (1), 349-358. 2015

Vahan Yoghourdjian, Tim Dwyer, Karsten Klein, Kim Marriott, and Michael Wybrow Graph Thumbnails: Identifying and Comparing Multiple Graphs at a Glance IEEE Transactions on Visualization and Computer Graphics, 2018 E. coli PPI

Vahan Yoghourdjian, Tim Dwyer, Karsten Klein, Kim Marriott, and Michael Wybrow Graph Thumbnails: Identifying and Comparing Multiple Graphs at a Glance IEEE Transactions on Visualization and Computer Graphics, 2018

Cognitive Scalability of Network Visualisation Vahan Yoghourdjian, Yalong Yang, Lee Lawrence, Michael Wybrow, Tim Dwyer, Kim Marriott Hardness model Graph size Local measures Clutter (crossings) Τ Density (|𝐹| |𝑊|) = 2 Density = 4 Density = 6 Nodes (|𝑊|) Easy Hard

Part 2: Immersive Analytics Interactive data analysis using the surfaces and spaces around us

Credits Andrea Batch Uni Maryland Kim Marriott Barrett Ens Benjamin Lee Maxime Cordeil Peter Hoghton Tobias Czauderna Sarah Goodwin Falk Schreiber Matthias Bruce Thomas Andrew Cunningham Niklas Elmqvist Yalong Yang Bernie Jenny Benjamin Bach Klapperstueck UniSA Uni Maryland UniSA

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Immersive Analytics Goals: to remove barriers betwe ween people, , their data and the tools they use fo for analysis to support data understanding and decision making everywh where and by everyone to make embodied tools that are intuitive, , engaging, , and make the best possible use of all sensory channels. . Immersive Analytics is the use of engaging, embodied analysis tools to support data understanding and decision making.

Immersive Analytics Timeline 2017 2014 2015 2016 Oct. Feb. Nov. Mar. Jun. Dec. Oct. Nov.

Immersive Analytics Timeline 2019 2018

Immersive Analytics Book Marriott, Dwyer, Schreiber, Thomas, Klein, Steurzlinger, Itoh, Riche Eds. 1. What is Immersive Analytics? 2. Time to Reconsider the Value of 3D for Information Visualisation 3. Multisensory Immersive Analytics 4. Interaction for Immersive Analytics 5. Immersive Human-Centered Computational Analytics 6. Immersive Visual Data Stories 7. Situated Analytics 8. Collaborative Immersive Analytics 9. Just 5 Questions: toward a design framework for Immersive Analytics 10. Immersive Analytics Applications in Life and Health Sciences 11. Exploring Immersive Analytics For Built Environments Published 2018

Immersiv ive Analy lytic ics s Resea earch at Mo Monash

Immersive Collaborative Analysis of Network Connectivity: CAVE-style or Head-Mounted Display? Cordeil, Dwyer, Klein, Laha, Marriott, Thomas IEEE Transactions on Visualization and Computer Graphics 2016

Colla llaboratio ion: Positions and movements HMDs records

Colla llaboratio ion: Positions and movements CAVE2 records

ImAxes: Immersive axes as embodied affordances for interactive multivariate data visualisation. Cordeil, M., Cunningham, A., Dwyer, T ., Thomas, B. H., & Marriott, K. In Proc. ACM Symp. on User Interface Software and Technology (pp. 71-83). ACM UIST 2017

Multidimensional data FIXED ACIDITY VOLATILE ACIDITY DENSITY CHLORIDES SULFUR DIOXIDE TYPE ALCOHOL QUALITY ACIDITY RESIDUAL SULPHATES SUGARS

Parallel coordinates Scatterplot Matrix

Axes as embodied* affordances** * Dourish, P. (2004) ** DA Norman (2002)

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There Is No Spoon: Evaluating Performance, Space Use, and Presence with Expert Domain Users in Immersive Analytics. Batch A, Cunningham A, Cordeil M, Elmqvist N, Dwyer T, Thomas BH, Marriott K. IEEE Transactions on Visualization and Computer Graphics, 2019

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In-Situ Mixed Reality Data Visualisation