方法对比

	Main Path Analysis	Structural Variation Analysis (Chen)
领域	文献计量学	文献计量学
方法族	Process / pipeline	Process / pipeline
起源年份	1989	2012
提出者	Norman P. Hummon & Patrick Doreian	Chaomei Chen
类型	Citation-network traversal pipeline for knowledge trajectories	Network-perturbation pipeline predicting transformative potential
开创性文献	Hummon, N. P., & Doreian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1), 39-63.	Chen, C. (2012). Predictive effects of structural variation on citation counts. Journal of the American Society for Information Science and Technology, 63(3), 431-449.
别名	MPA, Citation Main Path Analysis, Knowledge Flow Path Analysis	SVA, Structural Variation Theory, Boundary-Spanning Citation Analysis
相关	3	3
摘要	Main path analysis (MPA) traces the principal trajectory of knowledge development through a citation network. Introduced by Norman Hummon and Patrick Doreian in their 1989 study of the discovery of DNA, the method treats a field's literature as a directed acyclic graph in which documents point backward in time to the work they cite. Rather than mapping the whole network, MPA weights each citation link by how central it is to the flow of ideas — how many knowledge-carrying paths run through it — and then extracts the chain of most-traversed links from the field's earliest sources to its most recent sinks. The result is a compact 'main path': an ordered sequence of papers that represents the backbone along which a research front actually developed.	Structural variation analysis (SVA), developed by Chaomei Chen in 2012, is a predictive bibliometric method that estimates the transformative potential of a newly published paper from how much it perturbs the existing structure of a field's literature. Building on the idea that scientific breakthroughs typically recombine previously disconnected bodies of knowledge, SVA represents a field as a baseline co-citation network and then measures the structural change a new paper introduces by adding the novel links implied by its reference list. Papers that forge boundary-spanning connections — bridging clusters that were formerly separate — are hypothesized to be more likely to attract future citations. Chen operationalized this with metrics such as the modularity-change rate, cluster linkage, and centrality divergence, and showed that they help predict a paper's eventual citation impact, giving the field an early, structural signal of potentially high-impact work.