Ensuring the proper and effective ways to visualize network data is important for many areas of academia, applied
sciences, the military, and the public. Fields such as social network analysis, genetics, biochemistry, intelligence,
cybersecurity, neural network modeling, transit systems, communications, etc. often deal with large, complex network
datasets that can be difficult to interact with, study, and use. There have been surprisingly few human factors
performance studies on the relative effectiveness of different graph drawings or network diagram techniques to convey
information to a viewer. This is particularly true for weighted networks which include the strength of connections
between nodes, not just information about which nodes are linked to other nodes. We describe a human factors study in
which participants performed four separate network analysis tasks (finding a direct link between given nodes, finding an
interconnected node between given nodes, estimating link strengths, and estimating the most densely interconnected
nodes) on two different network visualizations: an adjacency matrix with a heat-map versus a node-link diagram. The
results should help shed light on effective methods of visualizing network data for some representative analysis tasks,
with the ultimate goal of improving usability and performance for viewers of network data displays.
Identifying social network (SN) links within computer-mediated communication platforms without explicit relations
among users poses challenges to researchers. Our research aims to extract SN links in internet chat with multiple users
engaging in synchronous overlapping conversations all displayed in a single stream. We approached this problem using
three methods which build on previous research. Response-time analysis builds on temporal proximity of chat messages;
word context usage builds on keywords analysis and direct addressing which infers links by identifying the intended
message recipient from the screen name (nickname) referenced in the message [1]. Our analysis of word usage within
the chat stream also provides contexts for the extracted SN links. To test the capability of our methods, we used publicly
available data from Internet Relay Chat (IRC), a real-time computer-mediated communication (CMC) tool used by
millions of people around the world. The extraction performances of individual methods and their hybrids were assessed
relative to a ground truth (determined a priori via manual scoring).
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.