方法对比

	Author-Keyword Co-Occurrence Mapping	Burst Detection (Kleinberg) for Emerging Topics
领域	文献计量学	文献计量学
方法族	Process / pipeline	Process / pipeline
起源年份	1983	2003
提出者	Michel Callon, Jean-Pierre Courtial, William Turner & Serge Bauin; later Ying Ding, Gobinda Chowdhury & Schubert Foo	Jon Kleinberg
类型	Keyword co-occurrence network mapping pipeline	Temporal burst-detection pipeline for emerging terms and citations
开创性文献	Callon, M., Courtial, J.-P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191-235.	Kleinberg, J. (2003). Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery, 7(4), 373-397.
别名	Author Keyword Network Mapping, Keyword Co-Occurrence Analysis, Conceptual Structure Mapping	Kleinberg Burst Detection, Citation Burst Analysis, Burst Detection Algorithm
相关	3	3
摘要	Author-keyword co-occurrence mapping reveals the conceptual structure of a research field by analyzing the keywords authors attach to their papers. It is a form of co-word analysis, the technique Michel Callon and colleagues introduced in 1983 to study how scientific problems are constructed through the language of the literature. The premise is that keywords appearing together in the same documents are conceptually linked, so counting these co-occurrences across a corpus and normalizing them into association strengths yields a network in which terms cluster into coherent themes. Ying Ding, Gobinda Chowdhury, and Schubert Foo's 2001 study mapped information-retrieval research with exactly this approach, demonstrating its value for charting a field's topics. The method offers a content-based complement to citation-based maps, showing what a field is about rather than which works it cites.	Kleinberg burst detection identifies periods during which a feature in a document stream — a keyword, a phrase, or citations to a particular paper — suddenly surges in frequency, signaling an emerging topic or a moment of intense attention. Introduced by Jon Kleinberg in 2003 to find bursty structure in streams such as email and news, the algorithm models the arrival of events with an infinite-state automaton in which higher states correspond to faster emission rates. A burst is detected when the optimal explanation of the stream requires moving into a high-rate state, with a built-in cost that discourages spurious switching. In scientometrics the method has become a standard way to detect rising research terms and 'citation bursts' — papers or topics whose citation rate spikes — making sudden growth in the literature visible and datable.