Collective Dynamics of Complex Systems Research Group, Binghamton University, Binghamton, New York, United States of America.
PLoS One. 2012;7(6):e38747. doi: 10.1371/journal.pone.0038747. Epub 2012 Jun 13.
Researchers' networks have been subject to active modeling and analysis. Earlier literature mostly focused on citation or co-authorship networks reconstructed from annotated scientific publication databases, which have several limitations. Recently, general-purpose web search engines have also been utilized to collect information about social networks. Here we reconstructed, using web search engines, a network representing the relatedness of researchers to their peers as well as to various research topics. Relatedness between researchers and research topics was characterized by visibility boost-increase of a researcher's visibility by focusing on a particular topic. It was observed that researchers who had high visibility boosts by the same research topic tended to be close to each other in their network. We calculated correlations between visibility boosts by research topics and researchers' interdisciplinarity at the individual level (diversity of topics related to the researcher) and at the social level (his/her centrality in the researchers' network). We found that visibility boosts by certain research topics were positively correlated with researchers' individual-level interdisciplinarity despite their negative correlations with the general popularity of researchers. It was also found that visibility boosts by network-related topics had positive correlations with researchers' social-level interdisciplinarity. Research topics' correlations with researchers' individual- and social-level interdisciplinarities were found to be nearly independent from each other. These findings suggest that the notion of "interdisciplinarity" of a researcher should be understood as a multi-dimensional concept that should be evaluated using multiple assessment means.
研究人员的网络一直是活跃的建模和分析的对象。早期的文献主要集中在从注释科学出版物数据库重建的引文或合著网络上，这些网络有几个局限性。最近，通用网络搜索引擎也被用于收集有关社交网络的信息。在这里，我们使用网络搜索引擎重建了一个表示研究人员与其同行以及各种研究主题之间相关性的网络。研究人员与研究主题之间的相关性的特点是可视性提升——通过关注特定主题来增加研究人员的可见度。研究人员在同一研究主题上的可视性提升越高，他们在网络中的距离就越近。我们计算了研究主题的可视性提升与研究人员在个体水平（与研究人员相关的主题多样性）和社会水平（在研究人员网络中的中心度）上的跨学科性之间的相关性。我们发现，尽管某些研究主题的可视性提升与研究人员的整体知名度呈负相关，但与研究人员的个体水平跨学科性呈正相关。我们还发现，与网络相关的主题的可视性提升与研究人员的社会水平跨学科性呈正相关。研究主题与研究人员的个体和社会水平跨学科性之间的相关性几乎是相互独立的。这些发现表明，研究人员的“跨学科性”概念应该被理解为一个多维概念，应该使用多种评估手段来评估。
