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arXiv 0908.0449physics.soc-phphysics.data-an
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基于纵向引文数据的社团发现算法的稳定性研究

On the Stability of Community Detection Algorithms on Longitudinal Citation Data

Michael James Bommarito, Daniel Martin Katz, Jon Zelner

摘要:

引文网络和其他类型的图之间有着根本的区别。特别是,鉴于引用网络是有向的和非循环的,主要用于无向社交网络数据的方法可能会面临障碍。这对于引文网络中社区结构的动态发展尤其如此。也就是说,既不清楚何时适合采用现有的社区检测方法,也不清楚如何在现有方法中进行选择。使用模拟数据,我们试图澄清的条件下,应该使用现有的方法,这些算法是适当的,在给定的上下文中。我们希望这篇论文将作为一个有用的路标,并鼓励那些有兴趣开发更有针对性的方法,用于纵向引文数据。

英文摘要:

There are fundamental differences between citation networks and other classes of graphs. In particular, given that citation networks are directed and acyclic, methods developed primarily for use with undirected social network data may face obstacles. This is particularly true for the dynamic development of community structure in citation networks. Namely, it is neither clear when it is appropriate to employ existing community detection approaches nor is it clear how to choose among existing approaches. Using simulated data, we attempt to clarify the conditions under which one should use existing methods and which of these algorithms is appropriate in a given context. We hope this paper will serve as both a useful guidepost and an encouragement to those interested in the development of more targeted approaches for use with longitudinal citation data.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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