# Visual Analytics Course

### Site Tools

bordeaux20152016:bordeaux_edition

## Visual Analytics Course

Instructors: Guy Melançon (email: Guy dot Melancon at labri dot fr), Bruno Pinaud (email: Bruno dot Pinaud at labri dot fr)

### Bordeaux roadmap 2015-2016 / Session by session cutdown

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!!

#### Friday, September 11 (week 37, Bruno Pinaud)

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.

#### Friday, September 25 (week 39, Bruno Pinaud)

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:

No teaching.

#### Friday, October 9 (week 41, Bruno Pinaud, 2 courses)

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.

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

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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

#### Friday, October 23 (week 43, Guy Melançon, 2 courses)

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.

#### Friday, November 6 (week 45, Guy Melançon)

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.

#### Friday, November 13 (week 46, Guy Melançon)

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

TBA

Project coaching

TBA

#### January 15 (week 2, Guy Melançon and Bruno Pinaud, date to be confirmed)

Project evaluation