where Z ( α ) denotes ∑ L r = $_{1}$r − $^{α}$. Obviously, ( $^{a}$− 1)(2log2 + log Z ( a )) + a + 1 2( $^{a}$− 1) 2 is a monotonic increasing function. Thus, for any fixed c , -1 is the optimal exponent. There are 2 reasons. The first is that, according to our real so cial experience, we always make some new friends who are the friends of our friends. The second is that EM is a global optimal algorithm. Thus if we choose any node as our new friend, the result will be the same theoretically. VI. ABOUT THE EVOLUTIONARY MODEL (EM) A. Why we chose new friend only from the next nearest neighbors? B. How to Measure the Power Law Exponent in EM? To accurately measure the exponent value of power law distribution is not a easy work. Es- pecially, when the exponent is very close to − 1. We use the least square method to evaluate the exponent value. We are afraid the least square method is not a good way, so we plot the accu- mulated curve. Fortunately, it can be proved that when P ( r ) ∝ r − $^{1}$, the accumulated function in log-linear plot will be a straight line. We can see that the distribution is about P ( r ) ∝ r − $^{1}$. [1] Liben-Nowell, D., Novak, J., Kumar, R., Raghavan, P. and Tomkins, A. Geograph routing in social networks. Proc. Natl. Acad . 102 , 11623-11628 (2005). [2] http: // www.calit2.net / newsroom / article.php?id = 1558 and the up coming book: Christakis and Fowler: Connected: The Surprising Power of Social Networks and How They Shape Our Lives, Little Brown, (2009). 16