
LINQS
STATISTICAL RELATIONAL LEARNING GROUP @ UMD
A Dual-View Approach to Interactive Network Visualization
Visualizing network data, from tree structures to
arbitrarily connected graphs,
is a difficult problem in information visualization. A large
part of the problem is that in network data,
users
not only have to
visualize the attributes specific to each data item, but also the
links specifying how those items are connected to each
other.
Past approaches to resolving these difficulties focus on
zooming, clustering, filtering and applying various methods of laying
out nodes and edges. Such approaches, however, focus only on
optimizing a network visualization in
a single view,
limiting the
amount of information that can be shown and explored in
parallel. Moreover, past approaches do not allow users to cross
reference different subsets or aspects of large, complex networks.
In this paper, we propose an approach to these limitations
using multiple coordinated views of a given network. To illustrate our
approach, we implement a tool called DualNet and evaluate the tool
with a case study using an email communication network. We show how
using multiple coordinated views
improves navigation and provides
insight into large networks with multiple node and link properties and types.
BibTex references
@InProceedings\{namata:cikm07,
author = "Namata, Galileo Mark and Staats, Brian and Getoor, Lise and Shneiderman, Ben",
title = "A Dual-View Approach to Interactive Network Visualization",
booktitle = "ACM Conference on Information and Knowledge Management",
year = "2007",
}
![cikm0671-namata.pdf [385Ko]](/basilic/web/Publications/images/pdf.png)
![cikm0671-namata.ps [14.4Mo]](/basilic/web/Publications/images/ps.png)

