Monday, 15 February 2016

Network analysis paper: Weaving the fabric of science: Dynamic network models of science's unfolding structure

Weaving the fabric of science: Dynamic network models of science's unfolding structure

By Feng Shi, Jacob G. Foster, and James A. Evans


• 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.


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.


Link prediction; Hypergraphs; Random walks; Multi-mode networks; Science studies; Metaknowledge

Paper (open access) available at:

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