Lecture 12:

Network Visualization

Slides are modified from Lada Adamic, Adam Perer, Ben Shneiderman, and Aleks Aris

Outline

What is a network?

How do you analyze networks today?

What are the challenges?

How to integrate with other methods?

What are networks?

Networks are collections of points joined by lines.

“Network” ≡ “Graph”

points lines

vertices edges, arcs math

nodes links computer science

sites bonds physics

actors ties, relations sociology

node

edge

3

Network elements: edges

Directed (also called arcs) A -> B

A likes B, A gave a gift to B, A is B’s child

Undirected A <-> B or A – B

A and B like each other A and B are siblings A and B are co-authors

Edge attributes weight (e.g. frequency of communication) ranking (best friend, second best friend…) type (friend, relative, co-worker) properties depending on the structure of the rest of the graph:

e.g. betweenness

4

Planar graphs

A graph is planar if it can be drawn on a plane without any edges crossing

#s of planar graphs of different sizes

1:1

2:2

3:4

4:11

Every planar graph

has a straight line

embedding

Trees

Trees are undirected graphs that contain no cycles

Cliques and complete graphs

Kn is the complete graph (clique) with K vertices each vertex is connected to every other vertex there are n*(n-1)/2 undirected edges

K5 K8K3

Outline

What is a network?

How do you analyze networks today?

What are the challenges?

How to integrate with other methods?

Why Visualization?

Use the eye for pattern recognition; people are good at scanning recognizing remembering images

Graphical elements facilitate comparisons via length shape orientation texture Animation shows changes across time Color helps make distinctions Aesthetics make the process appealing

http://amaznode.fladdict.net/http://www.touchgraph.com/TGAmazonBrowser.html

Graph Drawing Aesthetics

Minimize edge crossings Draw links as straight as possible Maximize minimum angle Maximize symmetry Minimize longest link Minimize drawing area Centralize high-degree nodes Distribute nodes evenly Maximize convexity (of polygons) Keep multi-link paths as straight as

possible …

Source: Davidson & Harel

Node Placement Methods

Node-link diagrams Force-directed

Geographical maps

Circular layouts One or multiple concentric

Temporal layouts

Clustering

Semantic Substrates

Force-directed Layout

Also known as: Spring Spreads nodes

Minimizes chance of node occlusion

Geographical Map

Familiar location of nodes

Circular Layouts (1 circle)

Ex: Schemaball Database schema Tables connected via foreign keys

Circular Layouts (concentric)

Radial Tree Viewer

Circular (concentric) & Temporal

Hudson Bay Food Web

Temporal Layout

Clustering

Hierarchical Clustering

Semantic Substrates

Group nodes into regions According to an

attribute Categorical, ordinal, or

binned numerical

In each region: Place nodes according

to other attribute(s)

Force-directed

>30%

Familiar Layout

~30%

Circular Layout

~15%

Node layout strategy

First 100 in visualcomplexity.com

Statistics on Strategies

Outline

What is a network?

How do you analyze networks today?

What are the challenges?

How to integrate with other methods?

http://graphexploration.cond.org/index.html

Challenges of Network Visualization

Basic networks: nodes and links Node labels

e.g. article title, book author, animal name

Link labels e.g. Strength of connection, type of link

Directed networks Node attributes

Categorical (e.g. mammal/reptile/bird/fish/insect) Ordinal (e.g. small/medium/large) Numerical (e.g. age/weight)

Link Attributes Categorical (e.g. car/train/boat/plane) Ordinal (e.g. weak/normal/strong) Numerical (e.g. probability/length/time to traverse/strength)

C1) Basic Networks (nodes & links)

Power Law Graph 5000 nodes Uniformly distributed

C1) Basic Networks (continued)

Social friendship network 3 degrees from Heer 47,471 people 432,430 relations

C2) Node Labels

Adding labels Nodes overlap with other nodes Nodes overlap with links

250 nodes

C3) Link Labels

Challenges: Length Space Belongingness Distinction from other labels & other types of labels

C4) Directed Networks

Direction arrows labels Thickness color

SeeNet, Becker et al.

C5 & C6) Node & Link Attributes

Types: Categorical (e.g. mammal/reptile/bird/fish/insect) Ordinal (e.g. small/medium/large) Numerical (e.g. age/weight)

Value of node attribute indicated by node shape Value of link attribute indicated by a letter

C1

~12%C4

~10%

C2

~66%

Challenges

First 100 in visualcomplexity.com

Statistics on Challenges

C5

~10%

C6

~2%

C1) Basic networks

C2) Node labels

C3) Link labels

C4) Directed networks

C5) Node attributes

C6) Link attributes

Outline

What is a network?

How do you analyze networks today?

What are the challenges?

How to integrate with other methods?

Integrating with other methods

Social network analysis is inherently complex Analysts must understand every node's attributes as well

as relationships between nodes. The visualizations are helpful but too messy and

incomprehensible when data is huge.

Statistics are used to detect important individuals, relationships, and clusters,

Integrate this with

Network visualization in which users can easily and dynamically filter nodes and edges.

“Integrating Statistics and Visualization” by Adam Perer, Ben Shneiderman

Overview the network both statistically and visually

Present just sense of the structure, clusters and depth of a network Present some statistics to provide a way to both confirm and quantify the visual findings

Issues:

• Panning and zooming naively is not enough

• Zooming into sections of the network force users to lose the global structure.

Solution

• Allow user-controlled

Statistics to drive the navigation

Filter and Zoom to gain deeper insights

Users can select a node to see all of its attributes.

What do we achieve?

– “the ability to see each node and follow its edges to all other nodes.

Details on Demand

Outline

What is a network?

How do you analyze networks today?

What are the challenges?

How to integrate with other methods?