bordeaux20152016:bordeaux_edition

Instructors: Guy Melançon (email: `Guy dot Melancon at labri dot fr`

), Bruno Pinaud (email: `Bruno dot Pinaud at labri dot fr`

)

Remember that Tulip in installed in the CREMI in `/net/ens/tulip`

. It should always be the latest svn version compiled by hand. It can be launched with `/net/ens/tulip/bin/tulip`

or to directly launch the graph manipulation perspective `/net/ens/tulip/bin/tulip_perspective -p Tulip`

. If you find a bug, do not hesitate to show it to us!!

This first portion of the course is meant to introduce the topic of the course, motivate this still new research field although examples are there to indicate ideas go back to ancient times.

Have a look at the slides showed in the class.

It does include a comprehensive course on Tulip so students are able to work by themselves using the framework.

- InfoVis by examples
- Florence Nightingale, an example showing how visualization may be misleading …
- White house budget directly from the White House website
- Obama's 2012 Budget from the New-York Times website
- Visualization in action (using Tulip / directory import (tree layout, 2 1/2 D); poker players)

Because we want to actually 'do' things, we need to devote time learning how to use the Tulip framework (developed here at LaBRI). We worked on several examples showing many tulip functionalities:

- Visual analytics (scatterplot view, parallel coordinates view) with the poker players dataset,
- Data import from a csv file with the 2004 cars dataset. The data comes from the Interactive Data Visualization Book which is a recommended lecture.

No teaching.

Graph Drawing: Walker algorithm, sugiyama heuristics and introduction to graph drawing based on physical analogy. Slides showed during the lecture

See **Di-Battista, G.; Eades, P.; Tamassia, R. & Tollis, I.-G. Graph drawing – Algorithms for the visualization of graphs Prentice-Hall, 1999** for a nice introduction to graph drawing.

Hierarchical graph drawing (also called Layered drawing)

See this paper for a complete overview.

- Tulip
`python`

API (see Tulip's website) - Tulip
`python`

view

The Python part of the course or lesson will consists in:

- getting a graph (File/Import)
- we will start by making our own graph (a grid graph)
- you may also want to try the Grid, or Grid Approximation Tulip plugin

- computing a spanning tree out of it (python script)
- use a simple data structure like a stack or queue to drive the computation of a spanning tree, it's a few lines algorithm (python has a handy
`Queue`

library)

- applying a layout algorithm to the tree subgraph
- your spanning tree algorithm could well use a boolean property to select what edges (and all nodes) are selected to form the tree. Once you have that, use the
`addSubGraph`

method to build a subgraph - apply a tree layout algorithm to the tree. Use the
`applyLayoutAlgorithm`

method. It requires that you hand it a layout property. Use the`getLayoutProperty`

to grasp the`viewLayout`

property so the new positions for nodes will automatically be transferred to the whole graph (this is automagically done by Tulip. See the beginning of`main`

Python function in the Python script view.

- playing with node/edge properties to make this as readable as possible
- grasp the color property
`viewColor`

. Create and instance a light gray color`tlp.Color(192, 192, 192)`

or`tlp.Color.Gray`

for instance. iterate over edges and turn the non selected edges to gray (`viewColor.setEdgeValue(edge, tlp.Color.Gray)`

)

Here is a solution (will be available after the course). Have fun.

- You are asked to implement either
- Sindre's nested boxes (onion trees) algorithm for trees,
the H-tree layout by Eades*or*

- Your algorithm should start from an existing tree (use Tulip general tree random generator to test) and
from the previous layout to the next. The idea here is to force you to play with layout animation.*animate the change*

———————————————————

Multilevel graph navigation

Divide and conquer - drawing and manipulating graph hierarchies with Tulip We will use the following file to play with graph hierarchies infovis_clean.tlpx. The work to do is described in this document hierarchies.pdf.

No teaching

**Network metrics** This lecture was partly based on the chapter concerning network metrics. Half of the course was devoted to defining a few metrics on graphs: node degree (obvious while easily interpretable and extremely useful); clustering coefficient (also quite informative although more tedious to compute on some graphs); node betweenness centrality. We then looked into random walk version of node degree and (second order) centrality to scale the metrics to large graphs.

Graph clustering.

- Hierarchical agglomerative clustering for high-dimensional data. Need to define what a “good” clustering is for a graph – no Huygens criteria this time.
- Newman modularity is such a criteria.
- Implementation of a hierarchical agglomerative clustering using Newman's criteria, computing incremental improvements of cluster merge.

- Plotting (non graph) high-dimensional data (links with the MDS part of the course).

Clustering of multi-dimensional data (K-Means, CURE, …), hierarchical agglomerative clustering.

TBA

Project coaching. See term project for more information.

Project coaching

TBA

Project evaluation

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