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Dynamic Network Visualization: Methods for Meaning with Longitudinal Network Movies. James Moody, Daniel McFarland, and Skye Bender-deMoll
James Moody, Daniel McFarland, and Skye Bender-deMoll
This work is supported by an NSF Grant (IIS - 0080860 ) awarded to Moody, and a Research Incentive Award provided by Stanford University's Office of Technology and Licensing (Grant #2-CDZ-108) to McFarland. Thanks are extended to participants of James G. March’s Monday Munch at Stanford University and to participants of the Social Structure Research Group at the Ohio State University.
(1) Successive, agglomerating networks (Flipbooks)
(2) Successive, non-overlapping windows of time (Movies)
(3) Overlapping windows of time (Facilitates stability in movies)
Input matrix: all-pairs-shortest-path
Force model: springs between all pairs which relax to edge length
Optimization: each node has an "energy" according to "spring tension", node with highest energy is moved to optimal position using a Newton-Raphson steepest descent. Energy of network is minimized.
Input matrix: raw distance matrix
Force model: electrostatic repulsion between all, attraction to connected nodes, force minima is at desired edge length
Optimization: reposition nodes according to the force vector they "feel", the distance nodes are allowed to move is gradually decreased until graph settles.
Input matrix: raw similarity matrix
Model: nodes are repositioned to the weighted average of their peers' coordinates
Optimization: repeated iteration
Input matrix: all-pairs-shortest-path matrix or alternate measure of distances/similarities between nodes.
Model: 2D projection of high-dimensional space of the network using matrix algebra (generally SVD) to determine Eigenvectors or principal components which will display a large amount of variance.
Optimization: exact solution
Input matrix: all-pairs-shortest-path matrix or alternate measure of distances /similarities between nodes
Model: search for a low-stress projection from 2D projection of high-dimensional space of the network
Optimization: there are many different techniques, I don't know enough about them yet.
SoNIA - Social Network Image AnimatorSoNIA is a Java-based package for visualizing dynamic or longitudinal "network" data. By dynamic, we mean that in addition to information about the relations (ties) between various entities (actors, nodes) there is also information about when these relations occur, or at least the relative order in which they occur. Our intention for SoNIA is to read-in dynamic network information from various formats, aid the user in constructing "meaningful" layouts, and export the resulting images or "movies" of the network, along with information about the techniques and parameter settings used to construct the layouts, and some statistic indicating the "accuracy" or degree of distortion present in the layout.
Blue = Asymmetric nominations
Green = Symmetric nominations
Demonstrates how seemingly stable summary statistics on one network dimension can mask significant structural change on other dimensions, highlighting the holistic-view payoff to this technique.
Point – to see shifts in participation structures and process by which coordination and mobilization of students is accomplished.
Four task segments:
a. More teacher talk and very task-focused.
a. Shift in topic, not really shift in form.
a. Teacher drops from relevance and stable dyads/triads form.