SANE: Visual Analytics for Event-Based Diffusion on Networks

更广泛的研究背景：可视化分析（VA）显示了其在网络上沟通和调查信息传播过程的潜力。扩散过程是高度动态和随机的现象。为此，VA需要解决两个挑战：表示底层动态网络结构的进展和捕获过程的不确定性。大多数现有的VA方法近似的问题，通过施加一个离散的时间结构的输入数据和忽略不确定性。目的：通过关注单个传播事件的实时坐标（因此，基于事件），我们可以更好地近似真实世界的交互，这反过来又提高了分析和预测结果的可信度。然而，这大大增加了问题的复杂性，从算法和方法的角度来看，并需要修改现有的范例表示基于事件的不确定网络。我们称之为“VA基于事件的信息扩散与不确定性”。我们的目标是（i）提供VA中信息扩散域的数据任务表征，并在基于事件的网络上表示不确定性，（ii）贡献当前最先进的时态图布局算法，以提高其可访问性，并引入特定的布局策略，让底层扩散过程塑造最终的网络表示，和（iii）建立一个无缝的工作流程，可视化的事件为基础的扩散过程，作为一个共同的基础和灵感，在这一领域的进一步研究。方法：我们的设计方法基于Munzner等人的可视化设计和验证的嵌套模型和Miksch等人的设计三角形（数据-用户-任务范式）。对于域的数据-任务表征，我们参考Kerracher等人的工作。关于任务分类的验证和构建。为了验证我们的研究结果，我们将通过算法验证、专家对真实世界数据集的案例研究和用户研究来进行实验研究。创新：我们努力成为第一个系统地探索VA的设计空间，以实现基于事件的不确定性信息传播。我们通过创新的解决方案和方法调查了VA中有前途但目前代表性不足的研究趋势，为该领域当前的知识体系做出贡献，并开辟了新的令人兴奋的研究问题。参与的主要研究人员：该提案将两个着名的欧洲可视化分析研究小组结合在一起。Alessio Arleo担任PI，是TU Wien VA组的博士后研究员; Silvia Miksch教授（共同PI）是TU Wien的VA教授。根据WEAVE研究政策，科隆大学的共同申请人是可视化教授Tatiana Landesberger von Antburg教授。

Wider research context: Visual Analytics (VA) showed its potential in communicating and investigating information diffusion processes over networks. Diffusion processes are highly dynamic and stochastic phenomena. To this end, VA needs to tackle two challenges: representing the progression on the underlying dynamic network structure and capturing the uncertainty of the process. The majority of existing VA approaches approximate the problem by imposing a discrete time structure to the input data and disregarding uncertainties. Objectives: By focusing on the real-time coordinates of the individual propagation events (hence, event-based), we better approximate real-world interactions, which, in turn, improves confidence in the analysis and prediction results. However, this greatly increases the complexity of the problem from both an algorithmic and methodological perspective, and requires a revision of the existing paradigms for the representation of event-based uncertain networks. We call this problem "VA of Event-based Information Diffusion with Uncertainty". We aim to (i) provide a data-task characterization of the information diffusion domain in VA with a typology of the representation of uncertainty on event-based networks, (ii) contribute the current state-of-the-art of temporal graph layout algorithms to improve their accessibility and introduce specific layout strategies to let the underlying diffusion process to shape the final network representation, and (iii) establish a seamless workflow for the visualization of event-based diffusion processes, to serve as a common ground and inspiration for further research in this field. Approach: We base our design methodology on the nested model for visualization design and validation by Munzner et al. And the Design Triangle (Data-Users-Tasks paradigm) by Miksch et al. For the data-task characterization of the domain, we refer to the work by Kerracher et al. About validation and construction of task taxonomies. To validate our findings, we will resort to experimental studies through algorithmic validation, case studies with experts on real-world datasets, and user studies. Innovation: We strive to be among the first to systematically explore the design space of VA for event-based information diffusion with uncertainty. We investigate promising but currently under-represented research trends in VA with innovative solutions and approaches, to contribute to the current body of knowledge in the field and open new and exciting research questions. Primary researchers involved: This proposal puts together two renowned European research groups in Visual Analytics. Alessio Arleo acts as PI and is Post-Doc researcher in VA group at TU Wien; Prof. Silvia Miksch (co-PI) is Prof. in VA at TU Wien. The Co-Applicant from the University of Cologne, under the WEAVE research policy, is Prof. Tatiana Landesberger von Antburg, Prof. in Visualization.