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Visualizing Network Data

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)

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Visualizing Network Data

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  1. Visualizing Network Data Richard A. Becker et al. IEEE Transactions on Visualization and Computer Graphics March 1995 Presented by Haixia Zhao

  2. 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. (circuit-switched 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)

  3. 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

  4. 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.

  5. SeeNet • A network data visualization tool using • Static displays • Link maps • Node maps • Matrix displays • Interactive controls • Parameter focusing • Data filtering • Animation

  6. 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.

  7. 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.

  8. Overload into and out of the Oakland node(coded as segment thickness and color, using bisected segments to show the directions)

  9. Network-wide overload in the same time period

  10. 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.

  11. A node map of “call attempts”

  12. 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

  13. Network-wide overload using matrix display

  14. 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.

  15. Parameter focusing (cont.) • Dynamic parameter adjustment can help the analyst to choose proper parameter values

  16. 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.

  17. 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.

  18. Parameter focusing – Line shortening(network-wide overload)

  19. Parameter focusing – deactivating nodes(Percentage of idle network capacity into and out of one node near Chicago)

  20. 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.

  21. 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.

  22. 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

  23. 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.

  24. Direct Manipulation - Nodemap parameter controls • 3 vertical sliders: • symbol size • animation speed • color sensitivity level. • Controls the cutoff values for color changes.

  25. Direct Manipulation - Animation • Automatic animation: • Computer walks continuously over all the time periods. The animation speed is set by the Fast-Slow vertical slider. • Manual animation: • By dragging the time bar forward or backward, with the display updating continuously

  26. 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.

  27. 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

  28. 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.

  29. 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.

  30. Further examples • Apply SeeNet to a variety of situations: • CICNet packet-switched data network • An email communication network.

  31. 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)

  32. 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.

  33. Linkmap - WWW Traffic Primary connections from US to other countries.

  34. Strengths & Weaknesses • Strengths • Easy to understand • Weaknesses • No favorite sentence • Redundant

  35. 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.

  36. SeeNet3D • [Kenneth et al 1996] SeeNet3D expanded SeeNet in this paper, using 3D graphics • Some screenshots 3D linkmap (geographical & semantic)

  37. SeeNet3D A partially translucent arc map showing the WWW traffic.

  38. Cybernet • [Abel et al 2000] described CyberNet, a framework for managing networks using 3D metaphoric worlds.

  39. Geographic administration tool based on the building metaphore

  40. Topology administration tool based on the cone-tree metaphore

  41. Distributed system admin. tool based on the city metaphor

  42. Network traffic characterization tool based on a landscape metaphor

  43. Computer admin tool based on the solar system metaphor

  44. Node layout ([Zschech et al 2000]) • Tree layout using the radial technique in 2d and 3d [Eades & Whitesides 1994]

  45. Node layout ([Zschech et al 2000]) • Ring layout

  46. Node layout ([Zschech et al 2000]) • Sphere layout w/ the most important node in the center

  47. Node layout ([Zschech et al 2000]) • Hierarchical layout

  48. [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.

  49. The End

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