Explanation Through Network Visualization

Abstract

Assessments of configurations, dynamics, and cause and effect are at the heart of our thinking and explanation. Although numerous methods for such assessments have been developed and are being used in daily scientific practice, visualization is usually not considered to be one of them. In this article we first argue that this is due to the common practice of visualizing data rather than the information contained in it; second, we address a number of principles for effective visualization; and third, we assess visualizations generated by the software tool visone in order to explain network outcomes using these principles.