Linganisha mbinu

	Uchambuzi wa Usentrali	Muundo wa Mitandao ya Kielektroniki kwa Kina (ERGM / p*)	Utabiri wa Viungo
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	1979	1986 (foundational); modern ERGM framework 1996–2007	2003
Mwanzilishi	Linton C. Freeman	Frank & Strauss (1986); extended by Wasserman & Pattison (1996) and Robins et al. (2007)	—
Aina	Descriptive / exploratory network measure family	Probabilistic generative network model	Network inference task
Chanzo asilia	Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239.	Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29(2), 173-191.	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.
Majina mbadala	Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality	ERGM, p-star model, p* model, Üstel Rastgele Graf Modeli (ERGM / p*)	Bağlantı Tahmini (Link Prediction), missing link prediction, future link prediction, edge prediction
Zinazohusiana	5	6	5
Muhtasari	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.	The Exponential Random Graph Model (ERGM), also known as the p* model, is a statistical framework for network analysis that models the probability of an observed network as a function of its local structural features — such as reciprocity, triangles, and degree distribution. Developed from the foundational work of Frank and Strauss (1986) and extended into the modern framework by Wasserman and Pattison (1996) and Robins et al. (2007), ERGM is the inferential standard for social network analysis, capable of testing whether observed network structures arise by chance or reflect genuine social processes.	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.