这两个会都是VIS年会的重要组成部分，收录的文章相似，但还是有些区别。具体细节可参考VIS会议的官方网站: InfoVis和VAST。
录用率
往年这两个可视化子会录用的论文都收录到TVCG发表，因此录用率都很低。从15年开始，VAST的录用率稍微高一些，因为它会额外收录一些Conference-only Track的论文（例如2016年有17篇），Conference-only Track的文章意味着创新程度受到认可，但文章质量离TVCG还有一定差距。所以，要中可视化顶会的文章，VAST的路似乎更好走一些。
InfoVis 2016 (Submitted: 167, Accepted: 37, Acceptance Rate: 22.15%)
InfoVis 2015 (Submitted: 178, Accepted: 38, Acceptance Rate: 21.34%)
VAST 2016 (Submitted: 157, Accepted: 33+17, Acceptance Rate: 32%)
VAST 2015 (Submitted: 149, Accepted: 31+13, Acceptance Rate: 30%)
录用率可以参考这两个网址：ZJU VAG, Vis-Acceptance-Rates
论文结构
InfoVis
介绍，相关工作，方法总述，方法详述，实验案例，方法评估，讨论，总结
VAST
介绍，相关工作，待分析的问题，系统总述，数据分析，可视设计，实验案例和方法评估，讨论，总结（参考egoSlider论文）
论文类型
InfoVis
信息可视化是通过空间布局的方式表达数据的空间联系，例如：边绑定，大规模图的简化，平滑的数据显示技术，基于时间线的数据演化过程可视化，等等。
需要注意的是，如果文章涉及空间数据（例如：标量，矢量和张量），那就更适合投往SciVis。如果文章专注于可视分析，例如：一个通过使用可视交互技术来支持数据分析的工作，就比较适合投往VAST。
技术类（Technique）
技术类型的论文介绍了未出现过的新颖技术，或者显著地扩展了已有的技术。论文里呈现的技术或算法需要足够完整，以致于可以让一个可视化领域的研究生进行复现。作者还需要提供一个方法应用的原型。论文里需要引用相关论文，并讨论和证明论文的优点。当然针对数据集和缺点的讨论也是必要的。如果有评价部分（Evaluation），将能更好地提高论文的质量。
例子:
Steve Kieffer, Tim Dwyer, Kim Marriott, Michael Wybrow. HOLA: Human-like Orthogonal Network Layout. IEEE Transactions on Visualization & Computer Graphics, 22(1):349-358, 2016. (DOI)
Ali K. Al-Awami, Johanna Beyer, Hendrik Strobelt, Narayanan Kasthuri, Jeff W. Lichtman, Hanspeter Pfister, Markus Hadwiger. NeuroLines: A Subway Map Metaphor for Visualizing Nanoscale Neuronal Connectivity. IEEE Transactions on Visualization & Computer Graphics, 20(12):2369-2378, 2014. (DOI)
Samuel Gratzl, Nils Gehlenborg, Alexander Lex, Hanspeter Pfister, Marc Streit. Domino: Extracting, Comparing, and Manipulating Subsets across Multiple Tabular Datasets. IEEE Transactions on Visualization & Computer Graphics, 20(12):2023-2032, 2014. (DOI)
Michael J. McGuffin, Igor Jurisica. Interaction Techniques for Selecting and Manipulating Subgraphs in Network Visualizations. IEEE Transactions on Visualization & Computer Graphics, 15(6):937-944, 2009. (DOI)
VAST
在可视分析中，概念，理论，算法，技术，设计，系统，观察研究以及应用通常整合了数据分析，可视化以及交互式设计的方法，用以提升人机交互的能力。在这种背景下，可视分析就显得与众不同了。其使用的数据可以是时空或非时空，使用的技术可以是和人相关或和机器相关，应用领域可以是学术，产业界，商业界，或政府职能部门。因此，一篇可视分析的论文通常融合了多方面的技术和知识背景。
Technique and Algorithm
Visualization techniques in visual analytics processes.
Close integration of technical components of visual analytics (e.g., statistical analysis, data mining and machine learning algorithms, knowledge representations, visualization/interaction techniques and methodologies, etc.) for supporting visual data mining.
Visual analytics for supporting the advancement of non-visual technical components of visual analytics (e.g., visual analytics for supporting model selection and parameter setting, simulation, clustering and classification, learning, prediction, monitoring, and optimization).
Integrated data acquisition, management, retrieval, processing and transformation in visual analytics (e.g., multi-sources; multi-resolution; data provenance; uncertainty; real world measures; textual, audio, visual and other media; factual, statistical, semantic, synthesized, and hypothesized data; etc.).
VA techniques for spatial and non-spatial data, temporal data, streaming data, quantitative and qualitative data, text and document data, model visualization, and so on.
Techniques for production, presentation, and dissemination of VA results.
Examples:
C. Xie, W. Zhong and K. Mueller, “A Visual Analytics Approach for Categorical Joint Distribution Reconstruction from Marginal Projections” in IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 51-60, Jan. 2017. doi:10.1109/TVCG.2016.2598479. VAST 2016 Honorable Mention.
S. van den Elzen, D. Holten, J. Blaas and J. J. van Wijk, “Reducing Snapshots to Points: A Visual Analytics Approach to Dynamic Network Exploration” in IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 1-10, Jan. 31 2016. doi:10.1109/TVCG.2015.2468078. VAST 2015 Best Paper.
T. Mühlbacher and H. Piringer, “A Partition-Based Framework for Building and Validating Regression Models” in IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 12, pp. 1962-1971, Dec. 2013. doi:10.1109/TVCG.2013.125. VAST 2013 Best Paper.
Application
Delivering visual analytics solutions to applications in academic disciplines (e.g., physical sciences, biological and medical sciences, engineering sciences, social sciences, arts and humanities, and sports sciences).
Delivering visual analytics solutions to applications in industries and governance.
Delivering visual analytics solutions to applications in public services and entertainment (e.g., resilience, healthcare, transport, sports, tourism, broadcasting, and social media).
Examples:
D. Liu et al., “SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations” in IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, pp. 1-10, Jan. 2017. doi:10.1109/TVCG.2016.2598432.
F. Beck, S. Koch and D. Weiskopf, “Visual Analysis and Dissemination of Scientific Literature Collections with SurVis” in IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, pp. 180-189, Jan. 31 2016. doi:10.1109/TVCG.2015.2467757.
C. Shi, Y. Wu, S. Liu, H. Zhou and H. Qu, “LoyalTracker: Visualizing Loyalty Dynamics in Search Engines” in IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 1733-1742, Dec. 31 2014. doi:10.1109/TVCG.2014.2346912. VAST 2014 Honorable Mention.
