Ruling out static latent homophily in citation networks.
Ruling out static latent homophily in citation networks.
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DOI：
10.1007/s11192-016-2194-9
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发表时间：
2017
期刊：
影响因子：
3.9
通讯作者：
Nelhans G
中科院分区：
文献类型：
--
作者：
Wittek P;Darányi S;Nelhans G
引文和合著者网络提供了对科学进步动态的深入了解。我们也可以将它们视为因果结构的表示，即在图中捕获的逻辑过程。从因果关系的角度来看，我们可以问一些问题，比如作者是否主要因为他们之前的共同兴趣而形成群体，或者他们最喜欢的主题是否具有“传染性”并通过合著传播。这种网络已经被人工智能社区广泛研究，最近已经与量子物理中纠缠粒子产生的非局部相关性建立了联系-潜在隐藏变量的影响可以通过依赖于半定规划（SDP）松弛序列的相同代数几何方法来分析。沿着这条线索，我们将我们的样本合著者网络视为因果图，并使用SDP松弛，排除潜在的同质性仅作为先前共同兴趣的表现，从而导致观察到的模式性。通过引入代数几何引文研究，我们增加了一个新的工具，现有的方法分析内容相关的社会影响。
Citation and coauthor networks offer an insight into the dynamics of scientific progress. We can also view them as representations of a causal structure, a logical process captured in a graph. From a causal perspective, we can ask questions such as whether authors form groups primarily due to their prior shared interest, or if their favourite topics are ‘contagious’ and spread through co-authorship. Such networks have been widely studied by the artificial intelligence community, and recently a connection has been made to nonlocal correlations produced by entangled particles in quantum physics—the impact of latent hidden variables can be analyzed by the same algebraic geometric methodology that relies on a sequence of semidefinite programming (SDP) relaxations. Following this trail, we treat our sample coauthor network as a causal graph and, using SDP relaxations, rule out latent homophily as a manifestation of prior shared interest only, leading to the observed patternedness. By introducing algebraic geometry to citation studies, we add a new tool to existing methods for the analysis of content-related social influences.