Long ties, the social ties that bridge different communities, are widely
believed to play crucial roles in spreading novel information in social
networks. However, some existing network theories and prediction models
indicate that long ties might dissolve quickly or eventually become redundant,
thus putting into question the long-term value of long ties. Our empirical
analysis of real-world dynamic networks shows that contrary to such reasoning,
long ties are more likely to persist than other social ties, and that many of
them constantly function as social bridges without being embedded in local
networks. Using a novel cost-benefit analysis model combined with machine
learning, we show that long ties are highly beneficial, which instinctively
motivates people to expend extra effort to maintain them. This partly explains
why long ties are more persistent than what has been suggested by many existing
theories and models. Overall, our study suggests the need for social
interventions that can promote the formation of long ties, such as mixing
people with diverse backgrounds.

对真实世界动态网络的实证分析表明，长期关系比其他社交关系更有可能持续存在，并且许多这样的关系始终充当社交桥梁，而不嵌入到本地网络中。在机器学习的基础上使用了一种新的成本效益分析模型后，我们表明长期关系是更有益的，这部分解释了为什么长期关系比许多现有理论和模型所建议的更加持久。总的来说，我们的研究表明需要社交干预来促进长期关系的形成，例如混合不同背景的人。

研究和建模长关系的动态

Investigating and Modeling the Dynamics of Long Ties

Information theory can be used to analyze the cost-benefit of visualization
processes. However, the current measure of benefit contains an unbounded term
that is neither easy to estimate nor intuitive to interpret. In this work, we
propose to revise the existing cost-benefit measure by replacing the unbounded
term with a bounded one. We examine a number of bounded measures that include
the Jenson-Shannon divergence and a new divergence measure formulated as part
of this work. We use visual analysis to support the multi-criteria comparison,
narrowing the search down to those options with better mathematical properties.
We apply those remaining options to two visualization case studies to
instantiate their uses in practical scenarios, while the collected real world
data further informs the selection of a bounded measure, which can be used to
estimate the benefit of visualization.

本研究提出修改现有的成本效益测量方法，通过引入有界测量方法（如 Jenson-Shannon 散度和新的散度测量方法）进行多准则比较，并通过视觉分析和两个案例的研究应用最优方法估算可视化收益。

一种估算可视化效益的有界测量方法

A Bounded Measure for Estimating the Benefit of Visualization

Visualization and virtual environments (VEs) have been two interconnected
parallel strands in visual computing for decades. Some VEs have been purposely
developed for visualization applications, while many visualization applications
are exemplary showcases in general-purpose VEs. Because of the development and
operation costs of VEs, the majority of visualization applications in practice
are yet to benefit from the capacity of VEs. In this paper, we examine this
perplexity from an information-theoretic perspective. Our objectives are to
conduct cost-benefit analysis on typical VE systems (including augmented and
mixed reality, theatre-based systems, and large powerwalls), to explain why
some visualization applications benefit more from VEs than others, and to
sketch out pathways for the future development of visualization applications in
VEs. We support our theoretical propositions and analysis using theories and
discoveries in the literature of cognitive sciences and the practical evidence
reported in the literatures of visualization and VEs.

本研究旨在从信息理论的角度对虚拟环境系统（包括增强现实、混合现实、剧院系统和大型幕墙）进行成本效益分析，解释为什么一些可视化应用程序比其他应用程序更能从虚拟环境中受益，并勾勒未来虚拟环境中可视化应用程序发展的路径。