Effectively visualizing large networks through sampling
Effectively visualizing large networks through sampling
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DOI：
10.1109/vis.2005.25
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发表时间：
2005-11
期刊：
影响因子：
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通讯作者：
Davood Rafiei;Stephen Curial
中科院分区：
文献类型：
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作者：
Davood Rafiei;Stephen Curial
我们研究了可视化大型网络的问题，并开发了有效抽象网络的技术，并将网络大小减小到可以清晰查看的水平。我们的尺寸缩减技术是基于采样的，其中只有一个样本而不是整个网络被可视化。我们提出了一个随机的“焦点”概念，它指定了网络的一部分以及它需要放大的程度。可视化示例使我们的方法能够克服可视化大规模网络所固有的可伸缩性问题。我们报告了在大型网络中经常出现的一些特征，以及在从网络中采样时保留这些特征的条件。这在选择适当的抽样方案时是有用的，从而产生与原始网络具有相似特征的样本。我们的方法建立在关系数据库之上，因此可以使用任何现成的数据库软件轻松有效地实现它。作为概念验证，我们实现了我们的方法，并报告了我们在电影数据库和Web连接图上的一些实验。
We study the problem of visualizing large networks and develop techniques for effectively abstracting a network and reducing the size to a level that can be clearly viewed. Our size reduction techniques are based on sampling, where only a sample instead of the full network is visualized. We propose a randomized notion of "focus" that specifies a part of the network and the degree to which it needs to be magnified. Visualizing a sample allows our method to overcome the scalability issues inherent in visualizing massive networks. We report some characteristics that frequently occur in large networks and the conditions under which they are preserved when sampling from a network. This can be useful in selecting a proper sampling scheme that yields a sample with similar characteristics as the original network. Our method is built on top of a relational database, thus it can be easily and efficiently implemented using any off-the-shelf database software. As a proof of concept, we implement our methods and report some of our experiments over the movie database and the connectivity graph of the Web.