Download presentation
Presentation is loading. Please wait.
Published byAmbrose Evans Modified over 9 years ago
1
JOURNAL OF HUMAN–COMPUTER INTERACTION 2010 Ji Soo Yi, Niklas Elmqvist, and Seungyoon Lee.
2
Introduction Related work ◦ Node-Link Based Representations ◦ ZAME: Interactive Large-Scale Graph Visualization TimeMatrix User Study Conclusion Video Video
3
Visualization plays a crucial role in understanding dynamic social networks at many different levels (i.e., group, subgroup, and individual) node-link-based visualization techniques are currently widely used ◦ the edges are generally too narrow ◦ limitations in representing temporal changes ◦ a long period using animation or small multiples is challenging
4
a new approach to visualizing temporal social network data through a matrix-based visual representation, called “TimeMatrix”
5
multiple levels in analyzing social networks ◦ (a) the nodal and dyad levels ◦ (b) the subgroup level ◦ (c) the global level of entire network (Brass, Galaskiewicz, Greve, & Tsai, 2004; Contractor, Wasserman, & Faust, 2006)
7
temporal social network analysis should support the following tasks: ◦ Task 1—Analysis of temporal changes at the global level. ◦ Task 2—Analysis of temporal changes at the subgroup level. aggregation based on connectivity (Task 2a) aggregation based on node attributes (Task 2b) ◦ Task 3—Analysis of temporal associations among nodal and dyad level attributes. how node attributes (Task 3a) and edge attributes (Task 3b) change over time the temporal associations between these attributes can be examined (Task 3c) ◦ these three types of tasks can be performed in a simultaneous manner
8
the advantages: more intuitively understood and therefore better supporting user tasks such as clustering and path finding
9
ID123456 1110010 2101010 3010100 4001011 5110100 6000100 123456 1 2 3 4 5 6
11
A protein-protein interaction dataset (100,000 nodes and 1,000,000 edges) visualized using ZAME at two different levels of zoom.
12
detail level zero of this abstraction, the bottom level of the pyramid, is the adjacency matrix of the raw data
13
Categorical Attributes ◦ compute a distribution i.e. the count of each item aggregated per category Numerical Attributes ◦ mean, extreme(min/max), and median values Nominal Attributes ◦ such as article names, authors, or subject titles ◦ concatenation, finding common words, or sampling representative labels ◦ in ZAME: aggregate text by simply selecting the first label to represent the whole aggregate
14
Aggregated Visual Representations Standard color shade: Single color to show occupancy, or a two- color ramp scale to indicate the value. Average: Computed average value of aggregated edges shown as a “watermark” value in the cell. Min/max (histogram): Extreme values of aggregated edges shown as a smooth histogram. Min/max (band): Extreme values of aggregated edges shown as a band. Min/max (tribox): Extreme values of aggregated edges shown as a trio of boxes (the center box signifies the range). Tukey box: Average, minimum, and maximum values of aggregated edges shown as Tukey-style lines. Histogram (smooth): Four-sample histogram of aggregated edges shown as a smooth histogram. Histogram (step): Four-sample histogram of aggregated edges shown as a bar histogram.
15
easily and efficiently support large and dense social networks TimeCell: to displaying temporal data and statistical information on edges and nodes interaction techniques: ◦ semantic zooming, aggregation and node reordering allow for investigating a network at multiple granularity levels and layouts ◦ overlays and filters allow for comparing temporal data and statistical information
16
TimeCell: a visual aggregate that displays temporal information associated with a node or edge as a composite glyph this helps present individual level temporal statistics of edges on a single cell not only edges but also nodes on both the rows and column headers
18
When the screen allocation becomes too small, simple bar charts cannot be drawn on a TimeCell. when less than 100 pixels (10 pixels high and 10 pixels wide) are allotted for a TimeCell, bar charts in a TimeCell are replaced with a color shade glyph
19
the TimeMatrix matrix is a hierarchically aggregated structure ◦ can show more than one node or edge in the underlying graph data set ◦ useful for providing an overview of a data set ◦ to combine several semantically grouped nodes or edges into a single entity
20
to cope with the large amount of labels, statistics, and visual representations that can be associated with each TimeCell for both nodes and edges to visualize different types of edges different overlays can have different ranges in the X-axis and Y-axis ◦ to include the total ranges of different overlays
21
range slider can be associated with these various node or edge attributes
22
for example, when nodes are sorted by gender ◦ TimeCells can be clearly clustered into four categories: male-to-male, male-to-female, female- to-male, and female-to-female relationships (assuming that the underlying graph is directed). it is possible to utilize external statistical tools to generate proper clusters for rearranging the matrix
24
three researchers (P1, P2, P3) ◦ two graduate students and one faculty member (two female and one male) ◦ who had experience using node-link-based visualization tools Data set ◦ the records of interorganizational collaboration activities ◦ the data represent 730 unique organizations that participated in project implementation or knowledge sharing between 1987 and 2008
25
Data set ◦ Each node also has three different attributes: region (i.e., Asia, Africa, Europe, Latin America and the Caribbean, North America, Oceania) organization type (i.e., governmental, intergovernmental, nongovernmental, and private/for-profit) geographic scope (i.e., international, regional, and national). participants were asked to achieve two goals: ◦ (a) to examine the changing patterns of collaboration of the two types over time ◦ (b) to investigate the role of organizations of different types, regions, and geographic scopes in the collaboration activities
27
blue for joint implementation; red for knowledge-sharing
30
categorizing visual analytic tasks in temporal social network analysis (Tasks 1, 2, and 3), proposing an adjacency-matrix-based visual representation (TimeMatrix) for analyzing temporal graphs that complement node-link temporal graph visualization techniques, and supplementing TimeMatrix with interaction techniques supporting highly interactive visual exploration of real-world social networks across multiple levels of analysis.
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.