Graph Theory, Lecture 26: Random graphs I: elementary definitions and basic facts
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YouTubeReinhard Diestel: Graph theory lectures
Graph Theory, Lecture 26: Random graphs I: elementary definitions and basic facts
Precise notion of random graphs, _not_ appealing to intuition, slowly developed. (Basic probabilistic notions are explained, not assumed.) Examples: probabilities computed for some simple events. Probabilistic proof of Ramsey lower bound, Theorem 11.1.3, 35:25-45:00. Three definitions of the expectation E(X) of a non-negative random variable X ...
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