Algorithmic and Economic Aspects of Networks Nicole Immorlica Random Graphs What is a random graph?

Erdos-Renyi Random Graphs Specify number of vertices n edge probability p G(n,p) For each pair of vertices i < j,

create edge (i,j) w/prob. p Erdos-Renyi Random Graphs What does random graph G(n,p) look like? (as a function of p)

Random Graph Demo http://ccl.northwestern.edu/netlogo/models/ GiantComponent Properties of G(n,p) p < 1/n disconnected, small treelike components

p > 1/n a giant component emerges containing const. frac. of nodes Proof Sketch 1. Percolation 2. Branching processes

3. Growing spanning trees Percolation 1. Infinite graph 2. Distinguished node i 3. Probability p Each link gets ``open with probability p

Q. What is size of component of i? Percolation Demo http://ccl.northwestern.edu/netlogo/models/ Percolation

Percolation on Binary Trees V = {0,1}* E = (x,y) s.t. y = x0 or y00=0 x101 10 distinguished node 1 11

Def. Let (p) = Pr[comp() is infinite]. The critical threshold is pc = sup { p | (p) = 0}. Critical Threshold Def. Let (p) = Pr[comp() is infinite]. The critical threshold is pc = sup { p |

(p) = 0}. Thm. Critical threshold of binary trees is pc = . Prf. On board. Critical Threshold (p) 1

0 1/k p Thm. Critical threshold of k-ary trees is pc = 1/k.

Branching Processes Node i has Xi children distributed as B(n,p): Pr[Xi = k] = (n choose k) pk (1-p)(n-k) Q. What is probability species goes extinct? A. By percolation, if p > (1+)/n, live forever. Note extinction Exists i, X1 + + Xi < i.

Erdos-Renyi Random Graphs We will prove (on board) (1) If p = (1-)/n, then there exists c1 s.t. Pr[G(n,p) has comp > c1 log n] goes to zero (2) If p = (1+2)/n, then there exists c2 s.t. Pr[G(n,p) has comp > c2 n] goes to one First show (on board) (3) If p = (1+2)/n, then there exists c2, c3 s.t.

Pr[G(n,p) has comp > c2 n] > c3 Emergence of Giant Component Theorem. Let np = c < 1. For G G(n, p), w.h.p. the size of the largest connected component is O(log n). Theorem. Let np = c > 1. For G G(n,

p), w.h.p. G has a giant connected component of size ( + o(n))n for constant = c; w.h.p, the remaining components have size O(log n). Application Suppose the world is connected by G(n,p)

someone gets sick with a deadly disease all neighbors get infected unless immune a person is immune with probability q Analysis

1. Generate G(n,p) 2. Delete qn nodes uniformly at random 3. Identify component of initially infected individual Analysis Equivalently,

1. Generate G((1-q)n, p) 2. Identify component of initially infected individual Analysis By giant component threshold, p(1-q)n < 1 disease dies p(1-q)n > 1 we die

E.g., if everyone has 50 friends on average, need prob. of immunity = 49/50 to survive! Summary Random graphs G(n, c/n) for c > 1 have unique giant component

small (logarithmic) diameter low clustering coefficient (= p) Bernoulli degree distribution A model that better mimics reality? In real life

Friends come and go over time. Growing Random Graphs On the first day, God created m+1 nodes who were all friends And on the (m+i)th day, He created

a new node (m+i) with m random friends Mean Field Approximation Estimate distribution of random variables by distribution of expectations.

E.g., degree dist. of growing random graph? Degree Distribution Ft(d) = 1 exp[ -(d m)/m ] (on board) This is exponential, but social networks

tend to look more like power-law deg. distributions In real life The rich get richer much faster than the poor.

Preferential Attachment Start: m+1 nodes all connected Time t > m: a new node t with m friends distributed according to degree Pr[link to j] = m x deg(j) / deg(.) = m x deg(j) / (2mt) = deg(j) / (2t)

Degree Distribution Cumulative dist.: Ft(d) = 1 m2/d2 Density function: ft(d) = 2m2/d3 (heuristic analysis on board, for precise analysis, see Bollobas et al) A power-law! Assignment:

Readings: Social and Economic Networks, Chapters 4 & 5 M. Mitzenmacher. A brief history of generative models for power law and lognormal distributions. Internet Mathematics 1, No 2, 226-251, 2005. D.J. Watts, and S.H. Strogatz. Collective dynamics of small-world networks. Nature 393, 440-442, 1998.

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