Node-Attribute Graph Layout for Small-World Networks

Helen Gibson

Principal Supervisor: Dr. Paul Vickers1st Supervisor: Dr. Maia Angelova2nd Supervisor: Dr. Fouad Khelifi

Previous Supervisor: Dr. Joe Faith

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What is a Graph?

Relationships between concepts

Mathematics and Graph Theory

Graph

Graph Drawing

Information Visualisation

Network

Network Visualisation

http://visualcomplexity.com

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Examples

Social Networkshttp://on.fb.me/hy6dmb

Biological Networks

World Wide Webhttp://datamining.typepad.com/gallery/blog-map-gallery.html

IP Addresseshttp://circos.ca

https://www.fractalus.com/steve/stuff/ipmap/

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What’s the Problem?

Yeast interaction network in Gephi

It looks nice but is it doing anything useful?

Typical complaint: Giant-Hairball

Caused by force-directed algorithms

Old, but still popular and most commonly used

Connected nodes attract, other repel

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How Can This Be Solved?

Node Attributes

Example – Social Network

Node = PeopleLinks = Friendships

Attributes = age, gender, location, games they interact with, pages they had liked etc.

Typical Usage – As retinal variables

Use to tell us more information about the graph

Uses beyond retinal variables?

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Research Aims

Novel graph layout based on node-attributes

Many node attributes -> use a dimension reduction technique

Visual analysis of graphs

Visual Analytics - the science of analytical reasoning facilitated by interactive visual interfaces. [Thomas and Cook, 2005]

To further understand the connectivity and structure of the graph

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Node-Attributes to Dimensions

Attributes as a second set of linksNodes Attributes

Each attribute node is a dimension and existence of a link is a value for that dimension on that node

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Dimension Reduction and TPP

In visualisation: Many variables form a high-dimensional space reduce to

2 or 3 dimensions that can be seen on a display. Linear projections

Projection Pursuit: Finds the most ‘interesting’ projection Interestingness depends on the data

Targeted Projection Pursuit (TPP): Interactive Searches for a projection closest to a users desired view In following case, separation of the clusters as far as

possible.

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Small-World Networks

Networks that are: Highly clustered Smaller than average shortest path length

An Example: 4 clusters Small nodes are

attributes

Clustering – users’ most valued layout feature

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Force-Directed Graph+TPP

Comparison

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What’s Next?

‘How much better is the clustering?’

Real world domain applications

What do we learn about the data from the layout?

Evaluation

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Publications

Gibson, H. (2010) Data-driven layout for the visual analysis of networks. GROUP28: The XXVIII International Colloquium on Group-Theoretical Methods in Physics. Newcastle-upon-Tyne, July 2010. Poster presentation.

Gibson, H. , Faith, J. (2011) Node-attribute graph layout for small-world networks. 15th International Conference on Information Visualisation. London, July 2011