MIT14_15JF09_lec04

MIT14_15JF09_lec04 - 6.207/14.15: Networks Lecture 4:...

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Unformatted text preview: 6.207/14.15: Networks Lecture 4: Erdos-Renyi Graphs and Phase Transitions Daron Acemoglu and Asu Ozdaglar MIT September 21, 2009 1 Networks: Lecture 4 Outline Phase transitions Connectivity threshold Emergence and size of a giant component An application: contagion and diffusion Reading: Jackson, Sections 4.2.2-4.2.5, and 4.3. 2 Networks: Lecture 4 Phase Transitions for Erdos-Renyi Model Erdos-Renyi model is completely specified by the link formation probability p ( n ) . For a given property A (e.g. connectivity), we define a threshold function t ( n ) as a function that satisfies: P ( property A ) if p ( n ) t ( n ) 0, and P ( property A ) 1 if p ( n ) t ( n ) . This definition makes sense for monotone or increasing properties, i.e., properties such that if a given network satisfies it, any supernetwork (in the sense of set inclusion) satisfies it. When such a threshold function exists, we say that a phase transition occurs at that threshold. Exhibiting such phase transitions was one of the main contributions of the seminal work of Erdos and Renyi 1959. 3 Networks: Lecture 4 Threshold Function for Connectivity Theorem (Erdos and Renyi 1961) A threshold function for the connectivity of the Erdos and Renyi model is t ( n ) = log n ( n ) . To prove this, it is sucient to show that when p ( n ) = ( n ) log n ( n ) with ( n ) 0, we have P ( connectivity ) (and the converse). However, we will show a stronger result: Let p ( n ) = log n ( n ) . If < 1, P ( connectivity ) 0, (1) If > 1, P ( connectivity ) 1. (2) Proof: We first prove claim (1). To show disconnectedness, it is sucient to show that the probability that there exists at least one isolated node goes to 1. 4 Networks: Lecture 4 Proof (Continued) Let I i be a Bernoulli random variable defined as 1 if node i is isolated, I i = otherwise. We can write the probability that an individual node is isolated as q = P ( I i = 1 ) = ( 1 p ) n 1 e pn = e log ( n ) = n , (3) n where we use lim n 1 n a = e a to get the approximation. Let X = i n = 1 I i denote the total number of isolated nodes. Then, we have E [ X ] = n n . (4) For < 1, we have E [ X ] . We want to show that this implies P ( X = ) 0....
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MIT14_15JF09_lec04 - 6.207/14.15: Networks Lecture 4:...

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