विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| नेटवर्क प्रसार मॉडल× | केंद्रीयता विश्लेषण× | |
|---|---|---|
| क्षेत्र | नेटवर्क विश्लेषण | नेटवर्क विश्लेषण |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 1927 (epidemiological compartmental); 2003 (social influence cascade) | 1979 |
| प्रवर्तक≠ | Kermack & McKendrick (SIR/SIS, 1927); Kempe, Kleinberg & Tardos (Independent Cascade, 2003) | Linton C. Freeman |
| प्रकार≠ | Stochastic / deterministic simulation on graphs | Descriptive / exploratory network measure family |
| मौलिक स्रोत≠ | Kermack, W.O. & McKendrick, A.G. (1927). A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A, 115(772), 700-721. DOI ↗ | Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239. DOI ↗ |
| उपनाम | epidemic spreading models, compartmental models, influence propagation models, Ağ Yayılım Modelleri (SIR, SIS, Independent Cascade) | Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality |
| संबंधित | 5 | 5 |
| सारांश≠ | Network diffusion models are a family of compartmental and probabilistic frameworks that simulate how information, disease, or innovation spreads across a connected system. Rooted in the mathematical epidemiology of Kermack and McKendrick (1927), the SIR and SIS models partition nodes into states and track transitions driven by contact rates and recovery probabilities. The Independent Cascade and Linear Threshold models, formalised by Kempe, Kleinberg, and Tardos (2003), extend this logic to social influence, modelling how activation propagates through a network one neighbour at a time. | Centrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies nodes that broker information flow, closeness centrality captures proximity to all others, and eigenvector centrality (along with PageRank) rewards connection to highly connected neighbors. |
| ScholarGateडेटासेट ↗ |
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