当前位置:
X-MOL 学术
›
arXiv.cs.SI
›
论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Science and Technology Advance through Surprise
arXiv - CS - Social and Information Networks Pub Date : 2019-10-18 , DOI: arxiv-1910.09370 Feng Shi and James Evans
arXiv - CS - Social and Information Networks Pub Date : 2019-10-18 , DOI: arxiv-1910.09370 Feng Shi and James Evans
Breakthrough discoveries and inventions involve unexpected combinations of
contents including problems, methods, and natural entities, and also diverse
contexts such as journals, subfields, and conferences. Drawing on data from
tens of millions of research papers, patents, and researchers, we construct
models that predict next year's content and context combinations with an AUC of
95% based on embeddings constructed from high-dimensional stochastic block
models, where the improbability of new combinations itself predicts up to 50%
of the likelihood that they will gain outsized citations and major awards. Most
of these breakthroughs occur when problems in one field are unexpectedly solved
by researchers from a distant other. These findings demonstrate the critical
role of surprise in advance, and enable evaluation of scientific institutions
ranging from education and peer review to awards in supporting it.
中文翻译:
科技突飞猛进
突破性发现和发明涉及内容的意外组合,包括问题、方法和自然实体,以及各种背景,如期刊、子领域和会议。利用来自数千万篇研究论文、专利和研究人员的数据,我们构建了基于高维随机块模型构建的嵌入,以 95% 的 AUC 预测明年的内容和上下文组合的模型,其中新的概率为组合本身预测他们获得超额引用和主要奖项的可能性高达 50%。大多数这些突破发生在一个领域的问题被远方的研究人员意外解决时。这些发现提前证明了惊喜的关键作用,
更新日期:2020-01-17
中文翻译:
科技突飞猛进
突破性发现和发明涉及内容的意外组合,包括问题、方法和自然实体,以及各种背景,如期刊、子领域和会议。利用来自数千万篇研究论文、专利和研究人员的数据,我们构建了基于高维随机块模型构建的嵌入,以 95% 的 AUC 预测明年的内容和上下文组合的模型,其中新的概率为组合本身预测他们获得超额引用和主要奖项的可能性高达 50%。大多数这些突破发生在一个领域的问题被远方的研究人员意外解决时。这些发现提前证明了惊喜的关键作用,