Burst Detection (Kleinberg) for Emerging Topics
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.
पूरी विधि पढ़ें
यह खंड पढ़ने के लिए निःशुल्क खाते से साइन इन करें।
पद्धति मानचित्र
सम्बन्धित पद्धतियों का परिवेश — अन्वेषण हेतु किसी नोड का चयन करें।
स्रोत
- Kleinberg, J. (2003). Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery, 7(4), 373-397. DOI: 10.1023/A:1024940629314 ↗
इस पृष्ठ का उद्धरण कैसे दें
ScholarGate. (2026, June 23). Kleinberg Burst Detection for Emerging Topics and Citation Bursts. ScholarGate. https://scholargate.app/hi/bibliometrics/burst-detection-analysis
कौन-सी पद्धति?
इस पद्धति को उसकी निकटतम सजातीय पद्धतियों के साथ रखकर उन्हें साथ-साथ पढ़ें — पुस्तकालय पुस्तकें मेज़ पर रख देता है; चुनाव आपका है।
- Author-Keyword Co-Occurrence Mappingग्रंथमिति↔ तुलना करें
- Reference Publication Year Spectroscopy (RPYS)ग्रंथमिति↔ तुलना करें
- Structural Variation Analysis (Chen)ग्रंथमिति↔ तुलना करें