Updated graph-interfaces.txt

Jun Matsushita authored
revision 17009ddfccb7f4a21ad278d3f3d798ee210e57ab
graph-interfaces
# Graph Interfaces

## Linked Query Interface

### User Scenarios

Investigative journalism
Neurobiologist
Geographic / Urban navigation
Modeling
Knowledge Management

### Functional Interactions

The aim is to allow navigation/exploration/investigation into large and complex graph structures for instance:
- Selection: Quality clustering.

- Trajectory: Start from a selection and move in/out/sideways towards a new resulting selection
- Can be some kind of faceted column views approach
![Faceted Graph Parcours (Column Views)](images/photo-6jpg.txt)

- Pathways: Set 2 (or more) "pole" selections. Allow discovery of different pathways and connective structures.
- Patterns identification: Identify patterns/structures. Resonant selections (a la Gruff visual query on steroids): Select nodes, infer different unique properties, resonate with other structures (more strongly visually with stronger matches, fading towards less resonant structures...)

![Network Topology Vocabulary](http://blog.stephenwolfram.com/data/uploads/2013/04/network-topology-barchart2.png)

![Mini Social Networks](http://blog.stephenwolfram.com/data/uploads/2013/04/mininetwork-grid-large2.png)

### Adaptative Visualisations

Depending on intrinsic properties of the graph and domain based hints, different visualisations could be automatically suggested.
- Inputs
- Density of attributes
-

- Linking:
- Mapping of "significant" properties to specific visualisation parameters
- Display only interactive visualisation parameters that are significant (a la http://www.nytimes.com/interactive/2014/upshot/buy-rent-calculator.html?_r=0)

- Visualisation parameters
- 2D/Nodes
- Force directed:
- Chord
- Scatter
-

### Other inspiring data interactions

- Filter
![screen-shot-2014-06-12-at-175923.png](images/screen-shot-2014-06-12-at-175923.png)

- Highlight
![screen-shot-2014-06-12-at-180201.png](images/screen-shot-2014-06-12-at-180201.png)

- Projection

One dimensional
![screen-shot-2014-06-12-at-180320.png](images/screen-shot-2014-06-12-at-180320.png)

Two dimensional
![screen-shot-2014-06-12-at-175707.png](images/screen-shot-2014-06-12-at-175707.png)

Color
![screen-shot-2014-06-12-at-175529.png](images/screen-shot-2014-06-12-at-175529.png)
Automatic mapping of "significant" properties to specific visualisation parameters
- Prioritize/Display only interactive visualisation parameters that are significant (a la http://www.nytimes.com/interactive/2014/upshot/buy-rent-calculator.html?_r=0)

- Visualisation parameters
- Datum
- Nodes
- Graph
- Continuous (lines, surface,..)
- Space
- 2D/Nodes
- Force directed:
- Chord
- Scatter
- Time
- Cursor
- Projected (a la interlace)
- Ghosts (of future and past)
- Physics
- Color
- Friction
- Gravity
- Springs

![screen-shot-2014-06-12-at-181709.png](images/screen-shot-2014-06-12-at-181709.png)
http://kek.vis4.net/#/company/191

What are specifically "network data"/"linked data" visualisation parameters?

Overall layout
- network viz: force directed and so on....
- wolfram clustering can be a "summarization" projection but would still rely on a "network", which can also lead to a statistical view.

In a graph
- center
- clusters
- centralities http://en.wikipedia.org/wiki/Centrality
![](https://www.wolfram.com/mathematica/new-in-9/social-network-analysis/HTMLImages.en/centrality-and-prestige-of-florentine-families/O_3.png)

### Other inspiring data interactions

- Filter
![screen-shot-2014-06-12-at-175923.png](images/screen-shot-2014-06-12-at-175923.png)

- Highlight
![screen-shot-2014-06-12-at-180201.png](images/screen-shot-2014-06-12-at-180201.png)

- Projection

One dimensional
![screen-shot-2014-06-12-at-180320.png](images/screen-shot-2014-06-12-at-180320.png)

Two dimensional
![screen-shot-2014-06-12-at-175707.png](images/screen-shot-2014-06-12-at-175707.png)

Color
![screen-shot-2014-06-12-at-175529.png](images/screen-shot-2014-06-12-at-175529.png)

### Reading List

http://www.researchgate.net/publication/243601741_Co-evolution_of_Knowledge_Networks_and_21st_Century_Organizational_Forms_Computational_Modeling_and_Empirical_Testing

https://www.escholar.manchester.ac.uk/api/datastream?publicationPid=uk-ac-man-scw:169194&datastreamId=POST-PEER-REVIEW-PUBLISHERS.PDF

http://www.academia.edu/1343189/Trust_in_strategic_alliances_Toward_a_co-evolutionary_research_model

http://www.jstor.org/discover/10.2307/2463541?uid=3739448&uid=2&uid=3737720&uid=4&sid=21104141388557

http://people.maths.ox.ac.uk/~lee/schedule.pdf

http://link.springer.com/article/10.1186%2F2190-8532-2-10

http://www.samsi.info/sites/default/files/Carley_august2013_v2.pdf

http://www.oecd.org/corruption/acn/39971975.pdf

http://www.ey.com/Publication/vwLUAssets/Anti_Corruption/$FILE/Anti-Corruption-ebook-final.pdf