• Wenchao Li, Yuewen Gao, Yu He, Cong Zhu, and Ke Xu

• ACM Conference on Human Factors in Computing Systems (CHI), 2026

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Network visualization has become essential for understanding complex relationships across domains, yet network complexity creates an overwhelming exploration space where users frequently miss critical patterns. Existing tools often require predetermined analysis goals and manual workflow construction, limiting accessibility for non-experts. We present NetworkCanvas, a progressive network visualization system that guides users through personalized exploration via adaptive recommendations. Our approach combines a learning mechanism that adapts to user feedback, an analytic state graph preserving exploration provenance with branching paths, and a context-aware feedback interpreter that suggests analytical continuations based on selection patterns. Controlled studies demonstrate that NetworkCanvas users identified more noteworthy observations, reported higher confidence, and exhibited more systematic exploration compared to a baseline without recommendations. These results demonstrate that recommendation-guided exploration improves outcomes over unguided manual analysis; however, because our baseline lacked recommendation functionality entirely, the specific contribution of adaptive personalization versus static guidance remains an open question. Qualitative findings suggest that recommendations reduce analysis paralysis and support systematic exploration.

The authors would like to thank the experts and participants for their help in the project, as well as the anonymous reviewers for their valuable comments. This work was supported by the Natural Science Foundation of Jiangsu Province (Grant No. BK20251232) and the Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (JYB2025XDXM902).