Visualizing Network Data
R. A. Becker, S.G. Eick, A.R. Wilks
Reviewed by
Bill Kules and Nada Golmie
for CMSC 838S
Fall 1999
Outline
•
•
•
•
•
•
•
•
What is a network?
Challenges in visualizing large networks
Early work: knowledge bases (Fairchild)
Motivation for SeeNet
SeeNet project description
Critique
State of the Art
Demos
6/27/2016
Visualizing Network Data
2
What is a Network?
• Communication networks:
– Internet, telephone network, wireless network.
• Network applications:
– The World Wide Web, Email interactions
• Transportation network/ Road maps
• Relationships between objects in a data base:
– Function/module dependency graphs
– knowledge bases
6/27/2016
Visualizing Network Data
3
Challenges in Visualizing Large Networks
•
•
•
•
Positioning nodes
Managing link/ information
Graph scales
Navigation/ interaction
Layout of Internet routes and IP addresses from data collected in
September 1998, appeared in Wired Magazine December 1998 issue
6/27/2016
Visualizing Network Data
4
Early Work - Fairchild (1988)
• Representation of knowledge bases
– relationships among objects are represented as
directed graphs in 3D space.
• Platform requirements:
– identification of individual elements
– relative position of an element within a context
– explicit relationships between elements
• Main issues investigated are:
– Positioning, coping with large bases (Fisheye views),
navigation and browsing, dynamic execution of
knowledge base.
6/27/2016
Visualizing Network Data
5
Motivation for SeeNet
• SeeNet is a monitoring and visualization tool to
display and analyze large volumes of network
data and statistics (AT&T long distance network
traffic).
• Overcome the display clutter problem associated
with large networks:
– Interactive techniques
– More traditional methods such as aggregation,
averaging, and thresholding.
6/27/2016
Visualizing Network Data
6
SeeNet Project Description
SeeNet is designed to address the display
clutter problem. It consists of a collection of
graphical tools that include techniques for:
• Static Display
• Interactive Controls
• Animation
6/27/2016
Visualizing Network Data
7
Static Network Display Features
• Linkmaps
– too complex resulting in display clutter problem
• Nodemaps (glyphs)
– node contains information/statistics
– tradeoffs with details information about particular links
• Matrix Display
– to/from nodes are assigned row/columns and matrix
cells are associated with links.
– Solves visual prominence and overplotting problem
– Gives up geographical information
– ordering of rows/columns may be important
6/27/2016
Visualizing Network Data
8
Parameter Focusing
• Controls network display characteristics and
provides dynamic parameter adjustment.
Parameter values and classes include:
– statistic, levels (probing, brushing)
– geography/ topology (zoom)
– time (averaging), aggregation, size and color.
• Main problems are:
– large range of values
– multi-parameters lead to confusing displays
– displays are sensitive to particular parameter
values
6/27/2016
Visualizing Network Data
9
Direct Manipulation
• Modify focusing parameters while continuously
provide visual feedback and update display
(fast computer response).
• Features include:
– Identification: highlight, color, shape
– Linkmap parameter control: line thickness, length,
color legend, time slider, animation
6/27/2016
Visualizing Network Data
10
More Direct Manipulation Features
– Matrix display parameter control: drag-and-drop
action, row/column reordering.
– Nodemap parameter control: symbol size, color
– Animation: analysis of time-varying data
– Zooming and Bird’s Eye view
– Conditioning: filtering, setting background
variables and displaying foreground parameters.
– Sound
6/27/2016
Visualizing Network Data
11
Application Examples
Department Email Communication
Patterns. Each node corresponds to a user,
and links encode the number of electronic
mail messages sent between the users.
6/27/2016
Worldwide Internet Traffic. Traffic on the
Internet, square root of packets transmitted from
country to country across the NSFNET backbone
during the first week of February 1993.
Visualizing Network Data
12
Favorite Sentence
“Our goal is to understand the data and not the
networks themselves.”
6/27/2016
Visualizing Network Data
13
Strengths
• Integrated techniques in one tool
– Node placement
– Graph scaling
– Manipulation
• Reducing clutter
• Good use of color
• Good overview of related work: paper
presentation is clear and well positioned in
context.
6/27/2016
Visualizing Network Data
14
Weaknesses
• Model is tailored for data networks
– Limited positioning capabilities
– New Jersey
– Doesn’t work as well for e-mail example
• Novice vs. power user
• Could be tedious to adjust parameters
– need to use it to find out
6/27/2016
Visualizing Network Data
15
Discussion
• Are there better ways of achieving our objective?
• Did we already know what we just learned?
– What is the predictive or insight value?
– Who would use the tool? Network engineer?
– Allows identification of interesting parameters, but
could be limited beyond that.
• Paradox of graph visualizations
– Adjacent nodes are seen as more related, but long
links are visually dominant
• How to explore structure - Fairchild
• How to explore statistics - Becker
6/27/2016
Visualizing Network Data
16
State of the Art :
Network Visualization Tools
• Network management
• Traffic management and monitoring
– Internet statistics, traffic analysis
• Performance measurement
– Application /program performance (e.g. TCP/IP)
• Graph editing
• Relational databases
• A list of tools is available from CAIDA (SDSC)
http://www.caida.org/Tools
6/27/2016
Visualizing Network Data
17
Demos
•
•
•
•
Internet Map
Visual Route
AS
Skitter
6/27/2016
Visualizing Network Data
18