CentralityBased

Centralitybased refers to methods and analyses that rely on node centrality measures to determine the relative importance or influence of elements within a network. The term is often used to describe strategies in which centrality scores guide decisions about node selection, intervention, or analysis, rather than evaluating nodes purely by structural position.

In practice, centralitybased methods compute one or more centrality metrics such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, or PageRank. Nodes are then ranked or grouped according to these scores, and downstream tasks—such as selecting seeds in influence maximization, targeting critical infrastructure, or identifying influential users—are performed using those rankings.

Key considerations include the choice of metric, as different measures capture different notions of importance. Centrality can be sensitive to network size, density, and data quality, and may be misleading in dynamic, weighted, bipartite, or multilayer networks. Computational complexity varies by metric, with some centralities expensive on large graphs, though many efficient approximations exist. Centralitybased approaches are also commonly extended to weighted networks, temporal networks, and multilayer structures to better reflect real-world systems.

Extensions and variants include multi-criteria centrality, temporal or evolving-network centrality, and context-aware centrality that incorporates node attributes or edge weights. These methods underpin tasks in social network analysis, transportation, epidemiology, biology, and information diffusion, where identifying key nodes or links is essential for understanding and influencing network behavior.