Weaving the fabric of science: Dynamic network models of science's unfolding structure
By Feng Shi, Jacob G. Foster, and James A. EvansHighlights
- • Our hypergraph framework captures the multi-mode, higher-order complexity of science.
- • Our random walk model powerfully predicts how science evolves.
- • Our approach reveals intriguing modal dispositions behind the advance of science.
- • We find that entities of one type typically connect through entities of another type.
- • We find a special bridging role for methods and chemicals in the fabric of science.
- • We find that adding more node types leads to superlinear improvements in prediction.
Abstract
Science
is a complex system. Building on Latour's actor network theory, we
model published science as a dynamic hypergraph and explore how this
fabric provides a substrate for future scientific discovery. Using
millions of abstracts from MEDLINE, we show that the network distance
between biomedical things (i.e., people, methods, diseases, chemicals)
is surprisingly small. We then show how science moves from questions
answered in one year to problems investigated in the next through a
weighted random walk model. Our analysis reveals intriguing modal
dispositions in the way biomedical science evolves: methods play a
bridging role and things of one type connect through things of another.
This has the methodological implication that adding more node types to
network models of science and other creative domains will likely lead to
a superlinear increase in prediction and understanding.
Keywords
Link prediction; Hypergraphs; Random walks; Multi-mode networks; Science studies; MetaknowledgePaper (open access) available at: http://www.sciencedirect.com/science/article/pii/S0378873315000118
