Visualizations can help amplify human cognition. In an era where networks are becoming increasingly complex, the desirability of tools to compare and contrast sets, relationships, and reach is significant. Motivated by a practical need articulated by corporate decision makers, this research presents our journey in designing and implementing bicentric diagrams, a novel graph-based set visualization technique. A bicentric diagram enables simultaneous identification of sets, set relationships, and set member reach in integrated ego networks of two focal entities. Our technique builds on the well-established theory of tie strength to visually group and position nodes. We illustrate the broad applicability of bicentric diagrams with examples from four diverse sample domains: university collaboration, technology co-occurrence, health app purchases, and innovation ecosystems network. We assess the value of our technique using an expert-based value-driven evaluation approach. The paper concludes with implications and a discussion of opportunities for implementation in real-world settings.
The Computational Enterprise Science Lab focuses on the design, analysis, and management of complex enterprise systems (e.g. organizations, supply chains, business ecosystems) using information visualization, modeling/simulation, and system science approaches.