Influencing Network Graph Perception and Judgment: Effects of Direct Connections, Base Rates, and Visual Layout Proximity on Social Network Analysis

• Rebecca Rhodes, Johns Hopkins University Applied Physics Laboratory
• Isaiah Harbison, Johns Hopkins University Applied Physics Laboratory
• Nathan Bos, Johns Hopkins University Applied Physics Laboratory
• Celeste Paul, U.S. Department of Defense
• Clay Fink, Johns Hopkins University Applied Physics Laboratory
• Anthony Johnson, Johns Hopkins University Applied Physics Laboratory

Abstract

Social network graphs are often used to help inform judgments in a variety of domains, such as public health, law enforcement, and political science. Across two studies, we examined how graph features influenced probabilistic judgments in graph-based social network analysis and identified multiple heuristics that participants used to inform these judgments. Study 1 demonstrated that participants’ judgments were influenced by information about direct connections, base rates, and layout proximity, and participants’ self-reported strategies also reflected use of this information. Study 2 replicated findings from Study 1 and provided additional insight into the hierarchical ordering of these strategies and the decision process underlying judgments from social network graphs.

• The Paper: Influencing Network Graph Perception and Judgment: Effects of Direct Connections, Base Rates, and Visual Layout Proximity on Social Network Analysis

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