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This lecture covers the concept of consensus in networked control systems, focusing on the design of graph weights to guarantee consensus. It discusses the Metropolis-Hasting model, the equal-neighbour model, and properties of adjacency matrices. The lecture also explores the implications of row-stochastic matrices, primitive graphs, and doubly stochastic matrices in ensuring consensus.