Linganisha mbinu

	Uchambuzi wa Ustahimilivu na Maambukizi ya Mtandao	Uchambuzi wa Usentrali	Uchambuzi wa Mitandao Mingi
Nyanja	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao	Uchanganuzi wa Mitandao
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	2000	1979	2013–2014 (formal mathematical framework)
Mwanzilishi	Albert, Jeong & Barabási	Linton C. Freeman	Kivelä et al. (2014); De Domenico et al. (2013)
Aina	Network robustness / vulnerability framework	Descriptive / exploratory network measure family	Graph-theoretic network model
Chanzo asilia	Albert, R., Jeong, H. & Barabási, A.L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378–382.	Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239.	Kivelä, M. et al. (2014). Multilayer Networks. Journal of Complex Networks, 2(3), 203–271.
Majina mbadala	network vulnerability analysis, attack tolerance analysis, Ağ Dayanıklılığı ve Güvenlik Açığı Analizi	Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality	multiplex network analysis, multiplex networks, Çok Katmanlı Ağ Analizi (Multiplex Networks)
Zinazohusiana	5	5	6
Muhtasari	Network resilience and vulnerability analysis is an analytical framework, formalised by Albert, Jeong, and Barabási (2000), that measures how a network degrades functionally as nodes or edges are progressively removed. By running targeted-attack simulations — removing the highest-centrality nodes first — and random-failure simulations — removing nodes at uniform probability — the framework identifies which structural elements are critical to network integrity and where infrastructure is most exposed.	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.	Multilayer network analysis is a graph-theoretic framework, formalised by Kivelä et al. (2014) and De Domenico et al. (2013), that represents the same set of nodes simultaneously across multiple relationship layers. Where a single-layer network collapses all relationships into one graph, the multilayer model preserves the distinct relational context of each layer — social platform, biological interaction type, or infrastructure tier — while also modelling how layers couple with each other through interlayer edges.