Visualizing Network DataOutlineWhat is a Network?Challenges in Visualizing Large NetworksEarly Work - Fairchild (1988)Motivation for SeeNetSeeNet Project DescriptionStatic Network Display FeaturesParameter FocusingDirect ManipulationMore Direct Manipulation FeaturesApplication ExamplesFavorite SentenceStrengthsWeaknessesDiscussionState of the Art : Network Visualization ToolsDemosVisualizing Network DataR. A. Becker, S.G. Eick, A.R. WilksReviewed by Bill Kules and Nada Golmiefor CMSC 838SFall 199901/14/19 Visualizing Network Data 2 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•Demos01/14/19 Visualizing Network Data 3What 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 bases01/14/19 Visualizing Network Data 4Challenges in Visualizing Large Networks •Positioning nodes•Managing link/ information•Graph scales•Navigation/ interactionLayout of Internet routes and IP addresses from data collected inSeptember 1998, appeared in Wired Magazine December 1998 issue01/14/19 Visualizing Network Data 5Early 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.01/14/19 Visualizing Network Data 6Motivation 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.01/14/19 Visualizing Network Data 7SeeNet Project DescriptionSeeNet is designed to address the display clutter problem. It consists of a collection of graphical tools that include techniques for:•Static Display•Interactive Controls•Animation01/14/19 Visualizing Network Data 8Static 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 important01/14/19 Visualizing Network Data 9Parameter 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 values01/14/19 Visualizing Network Data 10Direct 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, animation01/14/19 Visualizing Network Data 11More 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.–Sound01/14/19 Visualizing Network Data 12Application ExamplesDepartment Email Communication Patterns. Each node corresponds to a user, and links encode the number of electronic mail messages sent between the users.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 1993.01/14/19 Visualizing Network Data 13Favorite Sentence“Our goal is to understand the data and not the networks themselves.”01/14/19 Visualizing Network Data 14Strengths•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.01/14/19 Visualizing Network Data 15Weaknesses•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 out01/14/19 Visualizing Network Data 16Discussion•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 - Becker01/14/19 Visualizing Network Data 17State 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/Tools01/14/19 Visualizing Network Data 18Demos•Internet Map•Visual