  Sensemaking is the cognitive process of extracting information, creating
schemata from knowledge, making decisions from those schemata, and inferring
conclusions. Human analysts are essential to exploring and quantifying this
process, but they are limited by their inability to process the volume,
variety, velocity, and veracity of data. Visualization tools are essential for
helping this human-computer interaction. For example, analytical tools that use
graphical linknode visualization can help sift through vast amounts of
information. However, assisting the analyst in making connections with visual
tools can be challenging if the information is not presented in an intuitive
manner.
  Experimentally, it has been shown that analysts increase the number of
hypotheses formed if they use visual analytic capabilities. Exploring multiple
perspectives could increase the diversity of those hypotheses, potentially
minimizing cognitive biases. In this paper, we discuss preliminary research
results that indicate an improvement in sensemaking over the traditional
link-node visualization tools by incorporating an annotation enhancement that
differentiates links connecting nodes. This enhancement assists by providing a
visual cue, which represents the perceived value of reported information. We
conclude that this improved sensemaking occurs because of the removal of the
limitations of mentally consolidating, weighing, and highlighting data. This
study aims to investigate whether line thickness can be used as a valid
representation of VoI.
