Visualizing Network Data
Richard A. Becker et al.
IEEE Transactions on Visualization and Computer Graphics
March 1995
Presented by Haixia Zhao
Focus


Visualize the data associated with a network (instead
of simply visualizing the structure of the network
itself)
A Network consists of a set of nodes and links with
data associated with them.
– Geographical spatial layout v.s. abstract network. (circuitswitched network v.s. personal communication network)
– Direct v.s. indirect link data (link flow v.s., link capacity)
– Categorical v.s. quantitative link/node data type. (type of
link/node v.s. link’s capacity)
– Static v.s. dynamic data (capacity v.s. network flow in several
time periods)
Challenge

Coping with large data volumes
– Hundreds or thousands of nodes
– Thousands or tens of thousands of links
– Data from many time periods

Overcome the map clutter problem
Previous data-reduction methods
& drawbacks

Previous methods to reduce the amount of network
data
– Aggregation: for large numbers of links or nodes.
– Averaging: for large numbers of time periods
– Thresholding & exception reporting: for detecting changes.

Problem:
– May obscure important information.
SeeNet

A network data visualization tool using
– Static displays
• Link maps
• Node maps
• Matrix displays
– Interactive controls
• Parameter focusing
• Data filtering
– Animation
Dataset
Telecommunication traffic among the
110 switches in the AT&T network on
Oct. 17, 1989, the day of the San
Francisco earthquake.
 Data in focus: network capacity and the
trend of traffic flows.

Link maps
Draw nodes spatially (on a map), and
draw line segments between each pair
of nodes for which there is data.
 To show the statistic data of a link.

– Color, thickness, etc.
– Data for both directions:
• Split and use the half connected to a node to
show the data with that node as the originating node.
• To reduce clutter, If a value is zero, the corresponding
line segments is not drawn
• A negative data value can be shown using a
dashed line.
Overload into and out of the Oakland node
(coded as segment thickness and color, using bisected
segments to show the directions)
Network-wide overload in the same time period
Node maps
Aggregate link data at each node.
 Display node-oriented data by showing
a glyph or a symbol such as circle or
rectangle at each node on the map,
coding the statistic values with the
visual characteristics such as size,
shape, color of the glyph.

A node map of “call attempts”
Matrix display
Shows the data of each link of the
network.
 Solves two fundamental problems
encountered by the geographic display
of network links.

– Undue visual prominence may be given to
long lines.
– Long lines may overplot other lines
Network-wide overload using matrix display
Parameter focusing
Each static display is determined by a
group of display parameters as well as
by the particular network data.
 The effectiveness of static displays
heavily depends on how well those
parameters are chosen. For example,

– Choose glyph size range in a node map to reduce
overlapping.
Parameter focusing (cont.)

Dynamic parameter adjustment can
help the analyst to choose proper
parameter values
Parameter focusing (cont.)

Statistic: choose what statistic data to display, such as
absolute overload v.s. percentage overload. Transformations
may also be needed (square-root, logarithms, etc.)

Levels: choose what data to display and what data to
suppress, such as suppressing links with very low overload.

Geography/Topology: activate & deactivate nodes and
associated links in certain geographic area or out of the current
zoom sub-region, so the analyst can concentrate on the active
part.
Parameter focusing(cont.)

Time: choose what time point to display. The analyst can
focus on the most interesting periods and look for changes.

Aggregation: dynamically aggregate statistic data over
geographical regions or logical subsets of the network.

Size: adjust the overall size of the symbols drawn on the map,
such as the size range of the rectangles in the node map. Large
enough to convey information yet small enough to avoid
excessive interference with other symbols.

Color: adjust the threshold statistic value upon which the
symbols will be colored differently to show the difference.
Parameter focusing – Line shortening
(network-wide overload)
Parameter focusing – deactivating nodes
(Percentage of idle network capacity into and out of
one node near Chicago)
Direct Manipulation for
parameter focusing in SeeNet

Enable the analyst to select interesting
parameter values using direct
manipulation:
– Manipulate the display parameters
dynamically while watching instant
continuous visual feedback on the display.
Good parameter focusing is achieved
when the display shows meaningful
information about the data.
Direct Manipulation - Identification

Interactively identify nodes and links by
touching them with the mouse w/o
pressing the button
– Show node names, data values, etc.

Indicate an anchor node first, then
identify other nodes to show the the link
data between the nodes and the anchor
node.
Direct Manipulation - Linkmap parameter controls
3 vertical sliders: line length of links, line thickness, animation speed
2 horizontal controls: interactive color legend and time slider. The color legend also has a double edged slider that can be
used to filter out some lines
The time slider sets the current time period
Direct Manipulation - Matrix Display
parameter controls
Also use linkmap’s interactive color
legend and time slider parameter
controls.
 Additionally, it has the capability to
permute the rows and columns using a
drag-and-drop action.

Direct Manipulation - Nodemap
parameter controls

3 vertical sliders:
– symbol size
– animation speed
– color sensitivity level.
• Controls the cutoff values for color changes.
Direct Manipulation - Animation

Automatic animation:
– Computer walks continuously over all the time
periods. The animation speed is set by the FastSlow vertical slider.

Manual animation:
– By dragging the time bar forward or backward,
with the display updating continuously
Direct Manipulation - Zooming and
Bird’s-Eye
Center-to-edge sweeping to zoom into a
rectangle sub-region
 Maintaining a global context by
providing a bird’s-eye view on the upper
left corner.
 Pan to move to another sub-region.

Three interactions between Zoom and Links



Left: All line segments intersecting the display are
drawn (too busy)
Middle: any line segments with at least one endpoint
in the display are drawn
Right: only lines that both begin and end inside the
display (none in this case) are drawn
Direct Manipulation Conditioning

In case of multiple related statistic variables,
select an interesting range for one or more
background variables, and set the display to
show a foreground variable.
 The conditioning operation implement an
“and” operation. It filters out all links whose
background variables are not within the
selected ranges, visually showing the
intersection between the sets.
Direct Manipulation - Sound
Node state changes: activate –
deactivate
 Conveying slider values: varying pitch
that tracks the slider bar’s position
 Animation frame changes: bell ringing to
indicate the restart of animation.

Further examples

Apply SeeNet to a variety of situations:
– CICNet packet-switched data network
– An email communication network.
Nodemap- CICNet Internet Network Packet Flows
13 universities and research facilities. Big circles for routers at the
facilities. Small circles show LAN attached to the routers.
The underlying map is schematic, not geographic
Statistic data is shown for each router interface instead of a node (router)
Linkmap - Email communication




ATT Bell Lab email statistics during a year
Each node is an employee. A link shows the amount of email exchanged.
Nodes are positioned so that uses exchanging large amount of emails are close to each
other.
“Hastings” in the center is the resident computer expert and system administrator. Newer
employees are on the edge.
Linkmap - WWW Traffic
Primary connections from US to other countries.
Strengths & Weaknesses

Strengths
– Easy to understand

Weaknesses
– No favorite sentence
– Redundant
What happened in this topic?

Before this paper:
– [Bertin 1981] laid down some fundamental work of using both node and link
representations as well as matrix representations.
– [Fairchild et al 1988] desribed the SemNet system for displaying and
manipulating a 3d view of a large network (not data on the network)
– [Sarkar & Brown 1994] described a fisheye distortion for visualizing the
structure of sparse networks.
– [Erick & Wills 1993] use aggregation, hierarchical information, node
positioning, and linked display for investigating large abstract networks with
hierarchies. They use shape, color, and other visual characteristics coding
node information and color, line thickness coding link information.
– [NCSA 1991] added 3D graphics to display animations of Internet packet
traffic with the network backbone raised above the network map.
– [Koike 1994] described a system VOGUE to display communication
patterns in parallel processing computer systems. It used nodes and links
positioned in 3D and rendered w/ symbols, sizes, and colors. It allows
interactive selection of viewpoints.
SeeNet3D
[Kenneth et al 1996] SeeNet3D
expanded SeeNet in this paper, using
3D graphics
 Some screenshots

3D linkmap (geographical & semantic)
SeeNet3D
A partially translucent arc map showing the WWW traffic.
Cybernet

[Abel et al 2000] described CyberNet, a
framework for managing networks using
3D metaphoric worlds.
Geographic administration tool based on the building metaphore
Topology administration tool based on the cone-tree metaphore
Distributed system admin. tool based on the city metaphor
Network traffic characterization tool based on a landscape
metaphor
Computer admin tool based on the solar system metaphor
Node layout ([Zschech et al
2000])

Tree layout using the radial technique in 2d and 3d
[Eades & Whitesides 1994]
Node layout ([Zschech et al
2000])

Ring layout
Node layout ([Zschech et al
2000])

Sphere layout w/ the most important node in the
center
Node layout ([Zschech et al
2000])

Hierarchical layout

[Xiao & Milgram 1992] reviewed various
techniques for displaying depth information,
examined input devices used to interact with
a 3D space, summarized some issues in 3D
network visualization from psychological,
task-related and implementational viewpoints,
and designed a preliminary experimental
program for evaluating various network
visualization techniques.
The End