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	Analisi di Centralità	Previsione dei Collegamenti	Modelli di Diffusione di Rete
Campo	Analisi delle reti	Analisi delle reti	Analisi delle reti
Famiglia	Process / pipeline	Process / pipeline	Process / pipeline
Anno di origine	1979	2003	1927 (epidemiological compartmental); 2003 (social influence cascade)
Ideatore	Linton C. Freeman	—	Kermack & McKendrick (SIR/SIS, 1927); Kempe, Kleinberg & Tardos (Independent Cascade, 2003)
Tipo	Descriptive / exploratory network measure family	Network inference task	Stochastic / deterministic simulation on graphs
Fonte seminale	Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239.	Liben-Nowell, D. & Kleinberg, J. (2007). The Link-Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology, 58(7), 1019-1031.	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.
Alias	Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality	Bağlantı Tahmini (Link Prediction), missing link prediction, future link prediction, edge prediction	epidemic spreading models, compartmental models, influence propagation models, Ağ Yayılım Modelleri (SIR, SIS, Independent Cascade)
Correlati	5	5	5
Sintesi	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.	Link prediction is a network-analysis task that estimates which edges are missing from an observed graph or which edges are likely to form in the future. Formalised by Liben-Nowell and Kleinberg (2003, 2007), it covers a spectrum of approaches — from simple structural similarity indices such as Common Neighbors, Jaccard coefficient, and Adamic-Adar, to matrix factorisation, and graph neural network (GNN) methods — and is evaluated with AUC and Average Precision to account for the heavily imbalanced ratio of real to non-existing edges.	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.