Existing frameworks in visualization and HCI emphasize iteration, data grounding, and stakeholder needs; however, they have not fully explored how evaluation might persist across phases, adapt to compressed timelines, and aid stakeholder engagement and elicitation. Building on prior frameworks, we introduce Evaluation-first design EvalOps that centers evaluation as a material component in the design process, emphasizing tighter feedback loops, co-evaluation with stakeholders, malleable forms of evaluation, and goals-to-metrics grounding. We illustrate how EvalOps shapes design outcomes through two case studies of data-visualization and LLM-enabled reasoning tools, demonstrating how evaluation-driven design facilitates alignment and trust, uncovers opportunities earlier, and supports cohesiveness under rapidly changing constraints. We contrast EvalOps with current visualization design methodologies and discuss opportunities for expanding evaluation-centered framings to other active areas of design research.

This research studies community detection in multiplex dark networks. Our method seeks to intelligently select appropriate layers for aggregation to approximate communities in the whole network, while reducing the impact of over-modeling the network. Community evolution is explored as layers of different types of information are added to the partial picture of the network. We determine the set of dominant layers needed to produce similar community partitions to the established ground truth aggregate network. The identification of dominant layers enhances the selection of which layers to choose for aggregation purposes. This reduces redundancy and noise, and increases the optimization of the available data to produce the desired network partitions. We use normalized mutual index (NMI), purity, density, and modularity for methodology evaluation and comparison metrics.