這兩個會都是VIS年會的重要組成部分廊酣，收錄的文章相似食侮，但還是有些區(qū)別。具體細(xì)節(jié)可參考VIS會議的官方網(wǎng)站: InfoVis和VAST合砂。
錄用率
往年這兩個可視化子會錄用的論文都收錄到TVCG發(fā)表青扔，因此錄用率都很低。從15年開始翩伪，VAST的錄用率稍微高一些微猖，因為它會額外收錄一些Conference-only Track的論文（例如2016年有17篇），Conference-only Track的文章意味著創(chuàng)新程度受到認(rèn)可缘屹，但文章質(zhì)量離TVCG還有一定差距凛剥。所以，要中可視化頂會的文章轻姿，VAST的路似乎更好走一些犁珠。
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%)
錄用率可以參考這兩個網(wǎng)址：ZJU VAG, Vis-Acceptance-Rates
論文結(jié)構(gòu)
InfoVis
介紹傅瞻，相關(guān)工作，方法總述盲憎，方法詳述嗅骄，實驗案例，方法評估饼疙，討論溺森，總結(jié)
VAST
介紹，相關(guān)工作窑眯，待分析的問題屏积，系統(tǒng)總述，數(shù)據(jù)分析磅甩，可視設(shè)計炊林，實驗案例和方法評估，討論卷要，總結(jié)（參考egoSlider論文）
論文類型
InfoVis
信息可視化是通過空間布局的方式表達(dá)數(shù)據(jù)的空間聯(lián)系渣聚，例如：邊綁定，大規(guī)模圖的簡化僧叉，平滑的數(shù)據(jù)顯示技術(shù)奕枝，基于時間線的數(shù)據(jù)演化過程可視化，等等瓶堕。
需要注意的是隘道，如果文章涉及空間數(shù)據(jù)（例如：標(biāo)量，矢量和張量）郎笆，那就更適合投往SciVis谭梗。如果文章專注于可視分析，例如：一個通過使用可視交互技術(shù)來支持?jǐn)?shù)據(jù)分析的工作宛蚓，就比較適合投往VAST激捏。
技術(shù)類（Technique）
技術(shù)類型的論文介紹了未出現(xiàn)過的新穎技術(shù)，或者顯著地擴(kuò)展了已有的技術(shù)苍息。論文里呈現(xiàn)的技術(shù)或算法需要足夠完整缩幸，以致于可以讓一個可視化領(lǐng)域的研究生進(jìn)行復(fù)現(xiàn)壹置。作者還需要提供一個方法應(yīng)用的原型竞思。論文里需要引用相關(guān)論文，并討論和證明論文的優(yōu)點钞护。當(dāng)然針對數(shù)據(jù)集和缺點的討論也是必要的盖喷。如果有評價部分（Evaluation），將能更好地提高論文的質(zhì)量难咕。
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
在可視分析中课梳，概念距辆，理論，算法暮刃，技術(shù)跨算，設(shè)計，系統(tǒng)椭懊，觀察研究以及應(yīng)用通常整合了數(shù)據(jù)分析诸蚕，可視化以及交互式設(shè)計的方法，用以提升人機(jī)交互的能力氧猬。在這種背景下背犯，可視分析就顯得與眾不同了。其使用的數(shù)據(jù)可以是時空或非時空盅抚，使用的技術(shù)可以是和人相關(guān)或和機(jī)器相關(guān)漠魏，應(yīng)用領(lǐng)域可以是學(xué)術(shù)，產(chǎn)業(yè)界妄均，商業(yè)界柱锹，或政府職能部門。因此丰包，一篇可視分析的論文通常融合了多方面的技術(shù)和知識背景奕纫。
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.
