CS 6965: Advanced Data Visualization

The Study of Large and Complex Data

Spring 2018

Schedule at a Glance (subject to change, guest lectures marked with a *)

Mutually Inclusive Modules
HD: High-dimensional data visualization
TOPO: Topological abstraction and summarization
NV: Network visualization
PV: Personalized visualization

Weekly Schedule (subject to change)

Week 1

1. Scikit-learn tutorial.
2. Install and read the documentation of kepler-mapper.

1. Interactive Data Visualization for the Web, 2nd Ed. Scott Murray, 2017.
2. [LiuMaljovecWang2017]: Visualizing High-Dimensional Data: Advances in the Past Decade. Shusen Liu, Dan Maljovec, Bei Wang, Peer-Timo Bremer and Valerio Pascucci, IEEE Transactions on Visualization and Computer Graphics (TVCG), 2017.
3. [JeongZiemkiewiczFisher2009]: iPCA: An Interactive System for PCA-based Visual Analytics. Dong Hyun Jeong, Caroline Ziemkiewicz, Brian Fisher, William Ribarsky and Remco Chang, Computer Graphics Forum, 2009.

Week 2

1. [SinghMemoliCarlsson2007]: Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition. Gurjeet Singh, Facundo Mémoli, and Gunnar Carlsson, Eurographics Symposium on Point-Based Graphics, 2007
2. [LumSinghLehman2013]: Extracting insights from the shape of complex data using topology. P. Y. Lum, G. Singh, A. Lehman, T. Ishkanov, M. Vejdemo-Johansson, M. Alagappan, J. Carlsson, and G. Carlsson, Scientific Reports, 2013.
3. [vanderMaatenHinton2008]: Visualizing Data using t-SNE. Laurens van der Maaten and Geoffrey Hinton, Journal of Machine Learning Research, 9, pages 2579-2605, 2008.

1. [BrunoRomanoMazaika2017]: Longitudinal identification of clinically distinct neurophenotypes in young children with fragile X syndrome. Jennifer L. Bruno, David Romano, Paul Mazaika, Amy A. Lightbody, Heather Cody Hazlett, Joseph Piven, and Allan L. Reiss, PNAS, 114(40), pages 10767-10772, 2017.
2. [Carlsson2009]: Topology and data. Bulletin of the American Mathematical Society, 46, pages 255-308, 2009.
3. [DeyMemoliWang2016]: Multiscale Mapper: Topological Summarization via Codomain Covers. Tamal K. Dey, Facundo Memoli, and Yusu Wang, Proceedings of the 27th annual ACM-SIAM symposium on Discrete algorithms, pages 997-1013, 2016.
4. [DeyMemoliWang2017]:Topological Analysis of Nerves, Reeb Spaces, Mappers, and Multiscale Mappers. Tamal K. Dey, Facundo Memoli, and Yusu Wang, arXiv:1703.07387, 2017.
5. [NicolauLevineCarlsson2011]: Topology based data analysis identifies a subgroup of breast cancers with a unique mutational profile and excellent survival. Monica Nicolau, Arnold J. Levine, and Gunnar Carlsson, PNAS, 2011.

Week 3

1. [LeeVerleysen2009]: Quality assessment of dimensionality reduction: Rank-based criteria. John A. Lee and Michel Verleysen, Neurocomputing, 72, pages 1431-1443 2009.
2. Section 3.2 of [LiuMaljovecWang2017]: Visualizing High-Dimensional Data: Advances in the Past Decade. Shusen Liu, Dan Maljovec, Bei Wang, Peer-Timo Bremer and Valerio Pascucci, IEEE Transactions on Visualization and Computer Graphics (TVCG), 2017.
3. [GerberBremerPascucci2010]: Visual Exploration of High Dimensional Scalar Functions. Samuel Gerber, Peer-Timo Bremer, Valerio Pascucci and Ross Whitaker, IEEE Transactions on Visualization and Computer Graphics, 16(6), pages 1271 - 1280, 2010.

1. [BrownLiuBrodley2012]: Dis-Function: Learning Distance Functions Interactively. Eli T. Brown, Jingjing Liu, Carla E. Brodley, and Remco Chang, IEEE Conference on Visual Analytics Science and Technology, pages 83-92, 2012.
2. [YuanRenWang2013]: Dimension Projection Matrix/Tree: Interactive Subspace Visual Exploration and Analysis of High Dimensional Data. Xiaoru Yuan, Donghao Ren, Zuchao Wang, and Cong Guo, IEEE Transactions on Visualization and Computer Graphics (TVCG), 19(12), pages 2625 - 2633, 2013.
3. [TurkayLundervoldLundervold2012]: Representative factor generation for the interactive visual analysis of high-dimensional data. Cagatay Turkay, Arvid Lundervold, Astri Johansen Lundervold, and Helwig Hauser, IEEE Transactions on Visualization and Computer Graphics, 18(12), pages 2621 - 2630, 2012.
4. [TurkayFilzmoserHauser2011]: Brushing Dimensions - A Dual Visual Analysis Model for High-Dimensional Data. Cagatay Turkay, Peter Filzmoser, and Helwig Hauser, IEEE Transactions on Visualization and Computer Graphics (TVCG), 17(12), pages 2591 - 2599, 2011.
5. [PaulovichSilvaNonato2010]: Two-Phase Mapping for Projecting Massive Data Sets. Fernando V. Paulovich, Claudio T. Silva, and Luis G. Nonato, IEEE Transactions on Visualization and Computer Graphics, 16(6), pages 1281 - 1290, 2010.
6. [MokbelLueksGirbrecht2013]: Visualizing the quality of dimensionality reduction. Bassam Mokbel, Wouter Lueks, Andrej Gisbrecht, and Barbara Hammer. Neurocomputing, 112 (18), pages 109-123, 2012.
7. [AnandWilkinsonDang2012]: Visual Pattern Discovery using Random Projections. Anushka Anand, Leland Wilkinson, and Tuan Nhon Dang, IEEE Conference on Visual Analytics Science and Technology (VAST), 2012.
8. [ChengFuZhang1999]: Entropy-based Subspace Clustering for Mining Numerical Data. Chun-Hung Cheng, Ada Waichee Fu, and Yi Zhang. Proceedings of the 5th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), pages 84-93, 1999.
9. [LiuWangThiagarajan2015]: Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections. Shusen Liu, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer and Valerio Pascucci, Computer Graphics Forum (CGF), 34(3), pages 271-280, 2015.
10. [PiringerBergerKrasser2010]: HyperMoVal: Interactive Visual Validation of Regression Models for Real-Time Simulation. H. Piringer, W. Berger, and J. Krasser, Computer Graphics Forum (CGF), 29(3), pages 983-992, 2010.
11. [Rodriguez-MartinezGoulermasMu2010]: Automatic Induction of Projection Pursuit Indices. E. Rodriguez-Martinez, John Yannis Goulermas, Tingting Mu, and Jason F. Ralph, IEEE Transactions on Neural Networks, 21(8), 2010.

Week 4

1. Section 4 of [LiuMaljovecWang2017]: Visualizing High-Dimensional Data: Advances in the Past Decade. Shusen Liu, Dan Maljovec, Bei Wang, Peer-Timo Bremer and Valerio Pascucci, IEEE Transactions on Visualization and Computer Graphics (TVCG), 2017.

1. [SachaZhangSedlmair2016]: Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis. Dominik Sacha, Leishi Zhang, Michael Sedlmair, John A. Lee, Jaakko Peltonen, Daniel Weiskopf, Stephen C. North, Daniel A. Keim, IEEE Transactions on Visualization and Computer Graphics (TVCG), 23 (1), pages 241 - 250, 2016.
2. [WenskovitchCrandell Ramakrishnan2018]: Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics. John Wenskovitch, Ian Crandell, Naren Ramakrishnan, Leanna House, Scotland Leman, Chris North, IEEE Transactions on Visualization and Computer Graphics (TVCG), 24(1), pages 131 - 141, 2018.
3. [PiringerBergerKrasser2010]: HyperMoVal: Interactive Visual Validation of Regression Models for Real-Time Simulation. H. Piringer, W. Berger, J. Krasser, Computer Graphics Forum, 2010.
4. [ChanCorreaMa2013]:The Generalized Sensitivity Scatterplot. Yu-Hsuan Chan, Carlos D. Correa, and Kwan-Liu Ma, IEEE Transactions on Visualization and Computer Graphics (TVCG), 19(10), 2013.
5. [CaoGotzSun2011]: DICON: Interactive Visual Analysis of Multidimensional Clusters. Nan Cao, David Gotz, Jimeng Sun, and Huamin Qu, IEEE Transactions on Visualization and Computer Graphics (TVCG), 17(12), 2011.
6. [Chernoff1973]: The Use of Faces to Represent Points in K-Dimensional Space Graphically. Herman Chernoff, Journal of the American Statistical Association, 68(342), 1973.
7. [ElmqvistDragicevicFekete2008]: Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation. Niklas Elmqvist, Pierre Dragicevic, and Jean-Daniel Fekete. IEEE Transactions on Visualization and Computer Graphics (TVCG), 14(6), 2008.
8. [EtemadpourMottaPaiva2015]: Perception-Based Evaluation of Projection Methods for Multidimensional Data Visualization. Ronak Etemadpour, Robson Motta, Jose Gustavo de Souza Paiva, Rosane Minghim, Maria Cristina Ferreira de Oliveira, and Lars Linsen, IEEE Transactions on Visualization and Computer Graphics (TVCG), 21, 1, 2015.
9. [HoltenWijk2010]: Evaluation of Cluster Identification Performance for Different PCP Variants. Danny Holten and Jarke J. van Wijk, Computer Graphics Forum, 2010.
10. [KeimHaoLadisch2001]: Pixel Bar Charts: A New Technique for Visualizing Large Multi-Attribute Data Sets without Aggregation. Daniel Keim, Ming C. Hao, Julian Ladisch, Meichun Hsu, Umeshwar Dayal, IEEE Symposium on Information Visualization, 2001.
11. [SedlmairMunznerTory2013]: Empirical Guidance on Scatterplot and Dimension Reduction Technique Choices. Michael Sedlmair, Tamara Munzner, and Melanie Tory, IEEE Transactions on Visualization and Computer Graphics (TVCG), 19(12), 2013.
12. [GerberBremerPacucci2010]: Visual exploration of high dimensional scalar functions. S. Gerber, P. Bremer, V. Pascucci, and R. Whitaker, IEEE Transactions on Visualization and Computer Graphics, 16(6), pages 1271-1280, 2010.

Week 5

1. Section 5 and 6 of [LiuMaljovecWang2017]: Visualizing High-Dimensional Data: Advances in the Past Decade. Shusen Liu, Dan Maljovec, Bei Wang, Peer-Timo Bremer and Valerio Pascucci, IEEE Transactions on Visualization and Computer Graphics (TVCG), 2017.
2. [Moore Lecture: Information Gain]: Andrew Moore's lecture on Information Gain.
3. [Moore Lecture: Decision Trees]: Andrew Moore's lecture on Decision Trees.
4. [TensorFlow Playground]: Tinker With a Neural Network Right Here in Your Browser.
5. [TensorFlow: ML Beginners]: TensorFlow: Getting Started for ML Beginners.

1. [TensorFlow: Getting Started]: TensorFlow: Getting Started with TensorFlow.
2. [Deep Learning Tutorial 1]: Deep Learning Tutorial at Stanford University.
3. [Deep Learning Tutorial 2]: Neural Networks and Deep Learning by Michael Nielsen.
4. [Deep Learning Book]: Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.

Week 6

1. [SedlmairMeyerMunzner2012]: Design Study Methodology: Reflections from the Trenches and the Stacks. Michael Sedlmair, Miriah Meyer, Tamara Munzner, IEEE Transactions on Visualization and Computer Graphics (Proceedings of InfoVis 2012), 18(12): 2431-2440, 2012.

1. [McCurdyLeinColes2016]: Poemage: Visualizing the Sonic Topology of a Poem. Nina McCurdy, Julie Lein, Katharine Coles, Miriah Meyer. IEEE Transactions on Visualization and Computer Graphics (Proceedings of InfoVis 2015), 22(1):439-448, 2016.
2. [FerstayNielsenMunzner2013]:Variant View: Visualizing Sequence Variants in their Gene Context. Joel A. Ferstay, Cydney B Nielsen and Tamara Munzner. IEEE Trans. Visualization and Computer Graphics (Proc. InfoVis), 19(12):2546-2555, 2013.
3. [NielsenJackmanBirol2009]:ABySS-Explorer: Visualizing genome sequence assemblies. Nielsen CB, Jackman SD, Birol I, Jones SJM. IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2009). 2009. 15(6):881-8.
4. [Munzner2008]: Process and Pitfalls in Writing Information Visualization Research Papers. Tamara Munzner. Information Visualization: Human-Centered Issues and Perspectives. Andreas Kerren, John T. Stasko, Jean-Daniel Fekete, Chris North, eds. Springer LNCS Volume 4950, p 134-153, 2008.

Week 7

1. Read the documentation of TTK; follow the instructions to install TTK on your own computer.
2. Lecture Notes on Morse-Smale Complex by Prof. Guoning Chen.

1. [CarrSnoeyinkAxen2003]: Computing contour trees in all dimensions. Hamish Carr, Jack Snoeyink,Ulrike Axen. Computational Geometry, 24(2), pages 75-94, 2003.
2. [TiernyFavelierLevine2018]: The Topology ToolKit. Julien Tierny, Guillaume Favelier, Joshua A. Levine,Charles Gueunet, Michael Michaux. IEEE Transactions on Visualization and Computer Graphics, 24(1), pages 1077-2626, 2018.
3. [EdelsbrunnerHarerZomorodian2003]: Hierarchical Morse-Smale Complexes for Piecewise Linear 2-Manifolds. Herbert Edelsbrunner, John Harer, Afra Zomorodian. Discrete & Computational Geometry, 30(1), pages 87-107, 2003.

Week 8

1. [Forman2002]: A User's Guide To Discrete Morse Theory. Robin Forman. Séminare Lotharinen de Combinatore 48, 2002.

1. [CarraSnoeyinkPanne2010]: Flexible isosurfaces: Simplifying and displaying scalar topology using the contour tree. Hamish Carr, Jack Snoeyink, Michiel van de Panne. Computational Geometry: Theory and Applications, 43, pages 42-58, 2010.
2. [GuntherReininghausWagner2012]: Efficient computation of 3D Morse-Smale complexes and persistent homology using discrete Morse theory. David Günther, Jan Reininghaus, Hubert Wagner, Ingrid Hotz. The Visual Computer, 28(10), pages 959-969, 2012.
3. [KingKnudsonMramor2005]: Generating Discrete Morse Functions from Point Data. Henry King, Kevin Knudson, and Neza Mramor. Experimental Mathematics, 14(4), page 435, 2005.
4. [GuntherReininghausSeidel2014]: Notes on the Simplification of the Morse-Smale Complex. D Günther, J Reininghaus, HP Seidel, T Weinkauf. Topological Methods in Data Analysis and Visualization III, pages 135-150, 2014.

Week 9

1. [CarlssonnIshkhanovSilva2008]: On the Local Behavior of Spaces of Natural Images. Gunnar Carlsson, Tigran Ishkhanov, Vin de Silva, Afra Zomorodian. International Journal of Computer Vision, 76(1), pages 1 - 12, 2008.
2. [ChazalGuibasOudot2011]: Scalar Field Analysis over Point Cloud Data. Frédéric Chazal, Leonidas J. Guibas, Steve Y. Oudot, Primoz Skraba. Discrete & Computational Geometry, 2011.

1. [PereaCarlsson2014]: A Klein-bottle-based Dictionary for Texture Representation. J. A. Perea and G. Carlsson. International Journal of Computer Vision, 107(1), pages 75-97, 2014.
2. [Cohen-SteinerEdelsbrunnerHarer2007]: Stability of Persistence Diagrams. David Cohen-Steiner, Herbert Edelsbrunner, John Harer. Discrete & Computational Geometry, 37(1), pages103-120, 2007.

Week 10

1. Think about what personal data you might want to work with for Project 4.
2. Play with KDE and Kernel distance implementation from TDA-R.
3. Explore various physical visualization examples at http://dataphys.org/.

1. [PhillipsWnagZheng2015]: Geometric Inference on Kernel Density Estimates. Jeff M. Phillips, Bei Wang, and Yan Zheng. International Symposium on Computational Geometry (SOCG), 2015.
2. [BuchetChazalOudot2016]: Efficient and robust persistent homology for measures. Mickaël Buchet, Frederic Chazal, Steve Y. Oudot, Donald R. Sheehy. Computational Geometry: Theory and Applications, 58:70?96, 2016.
3. [VandeMoerePatel2009]: The Physical Visualization of Information: Designing Data Sculptures in an Educational Context. Andrew Vande Moere and Stephanie Patel, International Conference on Digital Interactive Media in Entertainment and Arts (DIMEA'08), ACM International Conference Proceeding Series Vol.349, Athens, Greece, pp. 343-350, 2009.

Week 11

Week 12

1. [MoerePatel2009]: The Physical Visualization of Information: Designing Data Sculptures in an Educational Context. Andrew Vande Moere and Stephanie Patel. Visual Information Communication, 2009.
2. [HuronJansenCarpendale]: Constructing Visual Representations: Investigating the Use of Tangible Tokens. Samuel Huron, Yvonne Jansen, Sheelagh Carpendale, IEEE Transactions on Visualization and Computer Graphics, 20(12), 2014.

Week 13

1. [FruchtermanReingold1991]: Graph drawing by force-directed placement. T. M. Fruchterman and E. M. Reingold. Software: Practice and experience, 21(11):1129?1164, 1991.
2. [TarawnehKellerEbert2011]: A General Introduction To Graph Visualization Techniques. Raga'ad M. Tarawneh, Patric Keller, and Achim Ebert. Proceedings of IRTG 1131 - Visualization of Large and Unstructured Data Sets Workshop 2011.
3. [ArchambaultMunznerAuber2007]: TopoLayout: Multi-Level Graph Layout by Topological Features. Daniel Archambault, Tamara Munzner and David Auber. IEEE Transactions on Visualization and Computer Graphics, 13(2), 2007.

1. [GajerGoodrichKobourov2004]: A multi-dimensional approach to force-directed layouts of large graphs. Pawel Gajer, Michael T. Goodrich, Stephen G. Kobourov. Computational Geometry, 29, 3-18, 2004.
2. [ArleoDidimoLiotta2016]: A Distributed Multilevel Force-directed Algorithm Alessio Arleo, Walter Didimo, Giuseppe Liotta, Fabrizio Montecchiani. Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD), 2016.

Week 14

1. [Holten2006]: Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data. Danny Holten. IEEE Transactions on Visualization and Computer Graphics, 12(5), pages 741-748, 2006.
2. Brain Connectivity Toolbox: List of network measures.

1. [ZhouXuYuan2013]: Edge Bundling in Information Visualization. Hong Zhou, Panpan Xu, Xiaoru Yuan, Huamin Qu. Tsinghua Science and Technology, 18(2), pages 145-156, 2013.
2. [Zhou2017]: A Survey of Edge Bundling Methods for Graph Visualization. Chaofeng Zhou, 2017.

Week 15

1. Semi-Supervised Learning Tutorial: Xiaojin Zhu, ICML 2007.
2. [KwonCrnovrsaninMa2018]:What Would a Graph Look Like in This Layout? A Machine Learning Approach to Large Graph Visualization. Oh-Hyun Kwon, Tarik Crnovrsanin, and Kwan-Liu Ma. IEEE Transactions on Visualization and Computer Graphics, 24(1), 2018.
3. [PientaAbelloKahng2015]: Scalable Graph Exploration and Visualization: Sensemaking Challenges and Opportunities. Robert Pienta, James Abello, Minsuk Kahng, Duen Horng Chau, International Conference on Big Data and Smart Computing (BigComp), 2015 .

1. Graph-Based Semi-Supervised Learning Tutorial: Zoubin Ghahramani, 2012.
2. [EadesHongKlein2017]: Shape-Based Quality Metrics for Large Graph Visualization. Peter Eades, Seok-Hee Hong, An Nguyen, Karsten Klein. Journal of Graph Algorithms and Applications, 21(1):29-53, 2017.
3. [VieiraNascimentoSilva2015]:The Application of Machine Learning to Problems in Graph Drawing A Literature Review. R. dos Santos Vieira, H. A. D. do Nascimento, and W. B. da Silva. Proc. International Conference on Information, Process, and Knowledge Management, pages 112-118, 2015.
4. [Masui1994]: Evolutionary Learning of Graph Layout Constraints from Examples. Toshiyuki Masui, Proc. ACM Symposium on User Interface Software and Technology, pages 103-108, 1994.
5. [OstingPalandeWang2017]: Towards Spectral Sparsification of Simplicial Complexes Based on Generalized Effective Resistance. Braxton Osting, Sourabh Palande, Bei Wang, 2017.

Week 16

Data on the Web:

Awesome Public Datasets
Stanford Large Network Dataset Collection (SNAP)

Coding resources on the Web:

Interactive Data Visualization for the Web, 2nd Edition by Scott Murray (2017)