Henr Nathalie, Bezerianos Anastasia, Fekete Jean-Daniel
INRIA-LRI.
IEEE Trans Vis Comput Graph. 2008 Nov-Dec;14(6):1317-24. doi: 10.1109/TVCG.2008.141.
Exploring communities is an important task in social network analysis. Such communities are currently identified using clustering methods to group actors. This approach often leads to actors belonging to one and only one cluster, whereas in real life a person can belong to several communities. As a solution we propose duplicating actors in social networks and discuss potential impact of such a move. Several visual duplication designs are discussed and a controlled experiment comparing network visualization with and without duplication is performed, using 6 tasks that are important for graph readability and visual interpretation of social networks. We show that in our experiment, duplications significantly improve community-related tasks but sometimes interfere with other graph readability tasks. Finally, we propose a set of guidelines for deciding when to duplicate actors and choosing candidates for duplication, and alternative ways to render them in social network representations.
探索社区是社交网络分析中的一项重要任务。目前，此类社区是通过聚类方法来识别，以便对参与者进行分组。这种方法通常会导致参与者只属于一个聚类，而在现实生活中，一个人可以属于多个社区。作为一种解决方案，我们建议在社交网络中复制参与者，并讨论这一举措的潜在影响。我们讨论了几种可视化复制设计，并使用对社交网络的图形可读性和视觉解释很重要的6项任务，进行了一项对比有复制和无复制情况下网络可视化的对照实验。我们表明，在我们的实验中，复制显著改善了与社区相关的任务，但有时会干扰其他图形可读性任务。最后，我们提出了一套准则，用于决定何时复制参与者以及选择复制对象，以及在社交网络表示中呈现他们的替代方法。
