Best Papers Committee

• Klaus Mueller, Stony Brook University
• Tamara Munzner, University of British Columbia
• Issei Fujishiro, Keio University

Best Papers Selection Process

• Reviewers have scored the paper submissions
• Chairs have selected the top six candidates (direct to TVCG) based on the paper quality and review scores
• Best Papers Committee has made their evaluation based on the significance of the paper to the field (presentation has not been considered)

Honorable Mention Pacific Graphics 2017

• Adaptive Disentanglement based on Local Clustering in Small-World Network Visualization

• - Arlind Nocaj, Mark Ortmann, and Ulrik Brandes

• This paper makes significant progress on one of the most central challenges that we face in network visualization: untangling hairball graphs. The authors propose using the clustering coefficient as an efficient proxy for underlying group structure, and automatically find an appropriate sparsification parameter to reveal it in their algorithm's drawings. Their careful validation is exemplary, with extensive computational benchmarks to show multiple metrics for both real-world datasets and synthetic data with known ground truth.

• PieceStack: Toward Better Understanding of Stacked Graphs

• - Tongshuang Wu, Yingcai Wu, Conglei Shi, Huamin Qu, and Weiwei Cui

• We award this paper an honorable mention since it makes significant contributions to the readability of stacked graphs. The proposed technique makes it much easier to understand which factors are the major contributors to the global shape of the graph and whether their trends are persistent or only occasional. A valuable element is also that the effectiveness of the technique is confirmed by a well conducted user study.

Best Paper of Pacific Graphics 2017

• Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion

• - Primoz Skraba, Paul Rosen, Bei Wang, Guoning Chen, Harsh Bhatia and Valerio Pascucci

• Recent advance in topology in visualization can be characterized with its extensions to higher spatiotemporal dimension and/or more structured (from a single scalar to multi-scalars, vector, and tensor). This paper indeed falls on the middle of airway in the specific topic, since it proposes a scalable robustness-driven simplification framework for 3D vector fields. If the authors have more subsequent results in this direction, our community will be able to tackle the long-cherished problem of delineating the topological structure of turbulent flow.

Best Visualization Note Award

• NetworkSeer: Visual Analysis for Social Network in MOOCs

• - Tongshuang Wu, Yuan Yao, Yuqing Duan, Xinzhi Fan, Huaming Qu, Hong Kong University of Science & Technology