Lecture 4 - Erdos-Renyi Graphs and Phase Transitio

# Lecture 4 - Erdos-Renyi Graphs and Phase Transitio -...

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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 Erd os-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 Erd os and Renyi 1959. 3 Networks: Lecture 4 Threshold Function for Connectivity Theorem (Erd os and Renyi 1961) A threshold function for the connectivity of the Erd os and Renyi model is t ( n ) = log ( n ) n . To prove this, it is sufficient to show that when p ( n ) = ( n ) log ( n ) n with ( n ) 0, we have P ( connectivity ) 0 (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 sufficient 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 I i = 1 if node i is isolated, 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) where we use lim n 1- a n n = e- a to get the approximation. Let X = n i = 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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## This note was uploaded on 04/17/2011 for the course ECONOMICS 14.14 taught by Professor Daronacemoglu during the Spring '11 term at MIT.

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Lecture 4 - Erdos-Renyi Graphs and Phase Transitio -...

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