lecture 12: network visualization slides are modified from lada adamic, adam perer, ben shneiderman,...
TRANSCRIPT
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?