Proceedings Article | 24 May 2012
Proc. SPIE. 8389, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR III
KEYWORDS: Visual analytics, Visualization, Matrices, Biochemistry, Telecommunications, Neural networks, Genetics, Applied sciences, Systems modeling, Social network analysis
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.
